FinalProject_Group035_WI24.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COGS 108 - The Effects of Sex, Event, and Bodyweight on Powerlifting Attempts\n",
    "\n",
    "# Permissions\n",
    "\n",
    "Place an `X` in the appropriate bracket below to specify if you would like your group's project to be made available to the public. (Note that student names will be included (but PIDs will be scraped from any groups who include their PIDs).\n",
    "\n",
    "* [ X ] YES - make available\n",
    "* [  ] NO - keep private\n",
    "\n",
    "# Names\n",
    "\n",
    "- Erick Amaro Hernandez\n",
    "- Sanjith Devineni\n",
    "- Alex Macias\n",
    "- Lawrence Ong\n",
    "- Mingyang Yao\n",
    "\n",
    "# Abstract\n",
    "\n",
    "In this project, we sought to find out whether sex or weight class influences the weight being lifted by an athlete in the sport of powerlifting. We began by hypothesizing a lot of things, which were based off of past EDA done on Kaggle by user Steve Wilson[CITE], and advice from coaches in the sport of powerlifting. From Wilson's previous findings that a given Sex's total was made up of different ratios of their best Squat, Bench, and Deadlift, we hypothesized that a Sex with a tendency to do better in an event would mean they would make bigger jumps in between the first and second attempt for a lift compared to a less performant Sex in that lift. For example, a Man's total weight lifted in a powerlifting competition was found to be made up more by their Bench Press than it was for a Female lifter. We thought this might result in a confidence boost and would lead to higher jumps in weight being taken because of this greater ability in the event. This sprouted to different factors that might affect someone's performance, so we extended the idea to weight classes since an athlete tends to lift more weight the heavier they are. To measure these jumps in weight from one attempt to another, we used the increase from one attempt to another in percentages. \n",
    "\n",
    "To measure these possible differences in weight selecting behaviour, we considered the OpenPowerlifting database and normalized the data to one standard due to the sport's changes across history and hundred of Federations managing the sport with different rules behind each one of them. For comparing weight class data in our visualizations, we went with a variation of the International Powerlifting Federation's weight classes. We visualized the gender disparity in the sport of powerlifting and across our defined weight classes, and continued to visualize their attempt selection seperated by Sex, Powerlifting Lift (Squat, Bench Press, or Deadlift), and by our normalized weight classes.\n",
    "\n",
    "We finished by running Linear regression on the data for a measurement of how much each of the variables we're looking at effect attempt selection, which negated most of our hypothesis. These results unexpectedly included that Women took greater jumps in weight between attempts than Men. The greatest difference in attempt selection on a per-event basis between the Sexes was in the Bench Press. This is surprising because this is often Female athletes' weakest lift, which went against our assumptions that if you are weak in a lift, then you might not have the confidence to take higher jumps. As for weight class. We finally looked at the effect of weight class on attempt selection, where we found no statistical difference in means, and incredibly low effect on attempt selection per kilogram of bodyweight."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our question is: Does weight class, event (type of lift: squat, bench press, or deadlift), and gender affect the percentage(%) increase in attempt weight selection(amount of weight the competitor chooses for their next attempt)? For example, given that an athlete has lifted 100kg in an event defined previously, would something like gender or weight class influence whether they pick 105 kgs, 110 kgs, or more for their next attempt? \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Background and Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The sport of powerlifting consists of 3 main **events**. The squat, the bench press, and the deadlift. A powerlifting competition gives you 3 attempts at the squat, 3 for the bench press, and 3 for the deadlift, and per event, each attempt must be heavier than the last (within the event), with the max weight lifted in each event summing up to the competitor's \"**total**\" for a meet. \n",
    "\n",
    "When competing in a powerlifting competition, choosing how much weight you'll be lifting has a lot of factors incorporated into it such as your own ability to judge the difficulty of your previous attempts and how you performed on those attempts. As such, attempt selection is an incredibly important skill a powerlifter must attain if they want to be successful in the sport. Seasoned coaches in the sport suggest that attempts start at around 90% of an athlete's max in the event. With 5% jumps between attempts being suggested from attempt 1 to 2, and <3% between attempt 2 and 3. It is also noted that heavier jumps may occur in the last event, the deadlift, due to it being the most exciting event of a powerlifting meet and/or the unique rules that come from it being the final event of a meet. <a name=\"cite_note-1\"></a> [<sup>1</sup>](#cite_ref-1) \n",
    "\n",
    "Most of the hype around the deadlift event comes from it contributing on average >40% of an athelete's total regardless of gender according to a previous analysis of the dataset by Kaggle user Steven Wilson <a name=\"cite_ref-2\"></a>[<sup>2</sup>](#cite_note-2). The importance of a lifter's deadlift fuels our hypothesis that maybe this lift comes with even higher/riskier jumps in weight per attempt on average. Another thing we can learn from Wilson's analysis and build on is that a larger portion of Men's totals is made up by their bench press, with it making up around 25% of their total in comparison to female lifters where it only makes up 20%. The study also shows the almost trivial trend of a higher athlete body weight being associated with higher totals.\n",
    "\n",
    "The prior work mostly looks at statistics of powerlifting like athlete totals rather than seeing some psychology behind the sport. We set out to uncover unconscious biases in weight selection for each attempt. If we discover that these biases exist and are significant, then this would help athletes plan out their attempts more efficiently. Athletes could then choose to be more conservative if planning a risky jump (being concious that they may be susceptible to biases found in this project), potentially adding more pounds or kilograms to their meet total rather than failing their next attempt or vice versa if we find out that their category tend to play more conservative. \n",
    "\n",
    "[^](#cite_ref-1) Krawczyk, Bryce (6 August, 2023) So You Wanna Be a Powerlifter? Attempt Selection During a Meet. *BarBend.com* https://www.barbend.com/powerlifting-meet-attempt-selection/\n",
    "\n",
    "[^](#cite-ref2) Steven Wilson (23 Sept. 2020) Strength Differences in Powerlifting by Gender https://www.kaggle.com/code/stevenwilson8/strength-differences-in-powerlifting-by-gender\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. We believe there will be no difference between genders when it comes to attempt selection in general. We think this because the different distributions of weight selection a per-event basis will balance eachother out to be equal when comparing just by gender. For example, although women may choose higher squat attempts, their bench attempts will might be lower and vice versa for men.\n",
    "2. The deadlift will have the higher jumps due to the hype around the amount of weight lifted and it being the main contributor to an athlete's total.\n",
    "3. Following from the last prediction, and from Wilson's past research on contribution of an event to an athlete's total, we predict that on a per-event basis there will be a difference between men and women's jumps, e.g. since more of men's totals is made up of the bench press, they might have higher jumps than women in the bench press.\n",
    "4. Based off our previous hypothesis and because heavier weight classes lift more weight, we think weight class will also effect % of weight increase.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data overview\n",
    "\n",
    "For each dataset include the following information\n",
    "- Dataset #1\n",
    "  - Dataset Name: Open Powerlifting Database\n",
    "  - Link to the dataset: https://www.kaggle.com/datasets/open-powerlifting/powerlifting-database\n",
    "  - Number of observations: 3,043,013\n",
    "  - Number of variables: 38\n",
    "\n",
    "The Openpowerlifting dataset is a collection of archived powerlifting competition results from 1946 - 1/1/2023.\n",
    "\n",
    "Every row of the OpenPowerlifting dataset consists of a record of how a lifter did during a competition. A row contains their age/weight data, what events they decided to compete in, whether they competed with equipment (certain competitions don't allow certain equipment to be used during a lift), geographic data like country, state, town, as well as the name of the Competition. It also includes rows of different scoring systems that take weight, gender, and total weight lifted in order to compare one's performance to another across varying weight/sex classes. All weight data is stored in Kilograms.\n",
    "\n",
    "Powerlifting is a sport where the philosophy behind the sport varies drastically. As such, there are results from 300+ federations in this dataset with their own rules behind what counts as a \"valid lift\". This also includes each federation having their own weight classes. Thankfully, the raw bodyweight a lifter weighed in at on the day of the competition is included as one of the columns, and 5 different scores are precomputed as columns if a lifter's performance was able to be scored.\n",
    "\n",
    "In order to compare lifters in this dataset, we will standardize to one weight class category, and one scoring system. Weight classes will be standardized to those that the International Powerlifting Federation uses (with some caveats, since the cutoffs are different for Female and Male lifters). DOTS will be our selected scoring system because it does not require that an athlete complete all 3 lifts to be calculated and is actively used today.\n",
    "\n",
    "We also need to calculate the jumps between a lifter's Squat 1 and Squat 2 attempt, which will be calculated in the following section. In this dataset, a failed attempt is set to the negative of the attempt weight. This means if there is a -100 in the Squat1Kg column, the lifter attempted 100kg and failed the attempt. Even if an attempt is failed, they are not allowed to lower the weight for the next attempt, so these 0% jumps should be ignored as to not skew any averages we compute."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OpenPowerlifting Databases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3043013, 41)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "df = pd.read_csv(\"./dataset/pl_data.csv\")\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NaN values\n",
    "\n",
    "Differences between how federations categorize their lifters are shown here, some recorded an age (Age), or just the range in which they fell into (AgeClass), some decide to combine the two in a \"Division\" column. There is also the \"Tested\" column which indicates whether or not athletes were drug tested, which is True if they were, NaN if they werent. Some Federations do not test for PEDs.\n",
    "\n",
    "Attempt values like Squat\\[1-3\\]kg are NaN if an athlete did not show up, or if an athlete decided to not do 3 attempts. There is also a 4th attempt column in the dataset, which is when there is an error in competition organization and the lifter is granted an extra attempt. \n",
    "\n",
    "BodyweightKg is also null if someone is No-Show to a competition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                      0\n",
       "Sex                       0\n",
       "Event                     0\n",
       "Equipment                 0\n",
       "Age                 1070337\n",
       "AgeClass             814256\n",
       "BirthYearClass      1000666\n",
       "Division               1435\n",
       "BodyweightKg          38368\n",
       "WeightClassKg         40757\n",
       "Squat1Kg            2137906\n",
       "Squat2Kg            2147142\n",
       "Squat3Kg            2171053\n",
       "Squat4Kg            3034560\n",
       "Best3SquatKg        1009169\n",
       "Bench1Kg            1652910\n",
       "Bench2Kg            1668528\n",
       "Bench3Kg            1710945\n",
       "Bench4Kg            3022026\n",
       "Best3BenchKg         358566\n",
       "Deadlift1Kg         2021904\n",
       "Deadlift2Kg         2039602\n",
       "Deadlift3Kg         2080157\n",
       "Deadlift4Kg         3020077\n",
       "Best3DeadliftKg      836823\n",
       "TotalKg              202421\n",
       "Place                     0\n",
       "Dots                 227397\n",
       "Wilks                227397\n",
       "Glossbrenner         227397\n",
       "Goodlift             476889\n",
       "Tested               804298\n",
       "Country             1284501\n",
       "State               2408023\n",
       "Federation                0\n",
       "ParentFederation    1068468\n",
       "Date                      0\n",
       "MeetCountry               0\n",
       "MeetState            859344\n",
       "MeetTown             411193\n",
       "MeetName                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Anomalies \n",
    "There are also some rows that are anomalies. The following competitor competed in a Bench Press/Deadlift-only competition, yet is missing bodyweight and has a best bench despite not having any Bench\\[1,2,3\\]Kg values filled in. It is important to note there is no official way to keep track of powerlifting events, so each is recorded differently through either a Excel- and we're subject to errors in data entry."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                              Fabricio Ubirajara de Assis\n",
       "Sex                                                         M\n",
       "Event                                                       B\n",
       "Equipment                                                 Raw\n",
       "Age                                                       NaN\n",
       "AgeClass                                                  NaN\n",
       "BirthYearClass                                          40-49\n",
       "Division                                                   MO\n",
       "BodyweightKg                                              NaN\n",
       "WeightClassKg                                            140+\n",
       "Squat1Kg                                                  NaN\n",
       "Squat2Kg                                                  NaN\n",
       "Squat3Kg                                                  NaN\n",
       "Squat4Kg                                                  NaN\n",
       "Best3SquatKg                                              NaN\n",
       "Bench1Kg                                                  NaN\n",
       "Bench2Kg                                                  NaN\n",
       "Bench3Kg                                                  NaN\n",
       "Bench4Kg                                                  NaN\n",
       "Best3BenchKg                                            200.0\n",
       "Deadlift1Kg                                               NaN\n",
       "Deadlift2Kg                                               NaN\n",
       "Deadlift3Kg                                               NaN\n",
       "Deadlift4Kg                                               NaN\n",
       "Best3DeadliftKg                                           NaN\n",
       "TotalKg                                                 200.0\n",
       "Place                                                       1\n",
       "Dots                                                      NaN\n",
       "Wilks                                                     NaN\n",
       "Glossbrenner                                              NaN\n",
       "Goodlift                                                  NaN\n",
       "Tested                                                    NaN\n",
       "Country                                                   NaN\n",
       "State                                                     NaN\n",
       "Federation                                            CONBRAP\n",
       "ParentFederation                                          GPC\n",
       "Date                                               2018-04-14\n",
       "MeetCountry                                            Brazil\n",
       "MeetState                                                 NaN\n",
       "MeetTown                                                  Jaú\n",
       "MeetName            Campeonato Paulista de Supino e Lev.Terra\n",
       "Name: 579, dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Name'] == \"Fabricio Ubirajara de Assis\"].iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dropping Data\n",
    "\n",
    "Since we are looking at attempt selection, we will be dropping any rows where the lifter signed up, but didn't compete. These can be found by those rows with NaN in their 1st attempt of every event, as well as those whose placement in a competition was \"NS\", for \"no-show\". \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Filter out any rows where there is no lift data, or lifter was no-show to the competition\n",
    "df = df.drop(['Goodlift','Wilks','Glossbrenner'], axis=1)\n",
    "df = df[~((df['Squat1Kg'].isna()) & (df['Bench1Kg'].isna()) & (df['Deadlift1Kg'].isna()))]\n",
    "df = df[df['Place'] != 'NS']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Standardizing Weight classes\n",
    "\n",
    "Earlier we discussed how there are 300+ federations, each with possibly their own defined weight classes. In this study, we will standardize the weight classes used by the International Powerlifting Federation, which hosts world championships, and can be seen as a \"Parent Federation\" in the dataset for federations worldwide, this means they all follow and conform to IPF guidelines to some degree. It's popularity worldwide made us choose it for this standardization. \n",
    "\n",
    "The weight classes are as follows, defined on [page 4 of the IPF rulebook](https://www.powerlifting.sport/fileadmin/ipf/data/rules/technical-rules/english/IPF_Technical_Rules_Book_2023__1_.pdf). A weight class like 80kg means you weigh under 80kg, but over the next smallest weight class. \n",
    "\n",
    "- Men\n",
    "    - \\[59kg, 66kg, 74kg, 83kg, 93kg, 105kg, 120kg, 120+kg\\]\n",
    "- Women\n",
    "    - \\[47kg, 52kg, 57kg, 63kg, 69kg, 76kg, 84kg, 84+kg\\]\n",
    " \n",
    "This however excludes people who do not identify as either (labeled as Mx in federations who support this), so we will merge the two categorizations to account for more bodyweights and identities. The following is the resulting weight classes, we use men's weight classes and append any lower weight classes that the women's classes support.\n",
    "\n",
    "\\[43kg, 47kg, 53kg, 59kg, 66kg, 74kg, 83kg, 93kg, 105kg, 120kg, 120+kg\\]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Standardizing weight classes\n",
    "def standardize_weight_class(weight):\n",
    "    if weight == np.nan:\n",
    "        return np.nan\n",
    "    \n",
    "    combined_ipf_classes = [43, 47, 53, 59, 66, 74, 83, 93, 105, 120]\n",
    "\n",
    "    unlimited_class = str(combined_ipf_classes[-1]) + \"+\"\n",
    "    for weight_class in combined_ipf_classes:\n",
    "        if weight < weight_class:\n",
    "            return str(weight_class)\n",
    "\n",
    "    return unlimited_class\n",
    "\n",
    "df['WeightClassKg'] = df['BodyweightKg'].apply(standardize_weight_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WeightClassKg\n",
       "83      278594\n",
       "93      243159\n",
       "105     218003\n",
       "74      215414\n",
       "120     152866\n",
       "66      143427\n",
       "120+    105640\n",
       "59       99374\n",
       "53       55073\n",
       "47       14903\n",
       "43        6980\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['WeightClassKg'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_absolute_value = df[['Squat1Kg','Squat2Kg','Squat3Kg','Bench1Kg','Bench2Kg',\n",
    "                        'Bench3Kg','Deadlift1Kg','Deadlift2Kg','Deadlift3Kg']].abs()\n",
    "\n",
    "df['squat12_percent_increase'] = ((df_absolute_value['Squat2Kg'] - df_absolute_value['Squat1Kg']) / df_absolute_value['Squat1Kg']) * 100\n",
    "df['squat23_percent_increase'] = ((df_absolute_value['Squat3Kg'] - df_absolute_value['Squat2Kg']) / df_absolute_value['Squat2Kg']) * 100\n",
    "\n",
    "df['bench12_percent_increase'] = ((df_absolute_value['Bench2Kg'] - df_absolute_value['Bench1Kg']) / df_absolute_value['Bench1Kg']) * 100\n",
    "df['bench23_percent_increase'] = ((df_absolute_value['Bench3Kg'] - df_absolute_value['Bench2Kg']) / df_absolute_value['Bench2Kg']) * 100\n",
    "\n",
    "df['Deadlift12_percent_increase'] = ((df_absolute_value['Deadlift2Kg'] - df_absolute_value['Deadlift1Kg']) / df_absolute_value['Deadlift1Kg']) * 100\n",
    "df['Deadlift23_percent_increase'] = ((df_absolute_value['Deadlift3Kg'] - df_absolute_value['Deadlift2Kg']) / df_absolute_value['Deadlift2Kg']) * 100\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def isNoIncrease(weightPercent):\n",
    "    if weightPercent == 0:\n",
    "        return np.nan\n",
    "    else:\n",
    "        return weightPercent\n",
    "\n",
    "df['squat12_percent_increase'] = df['squat12_percent_increase'].apply(isNoIncrease)\n",
    "df['squat23_percent_increase'] = df['squat23_percent_increase'].apply(isNoIncrease)\n",
    "df['bench12_percent_increase'] = df['bench12_percent_increase'].apply(isNoIncrease)\n",
    "df['bench23_percent_increase'] = df['bench23_percent_increase'].apply(isNoIncrease)\n",
    "df['Deadlift12_percent_increase'] = df['Deadlift12_percent_increase'].apply(isNoIncrease)\n",
    "df['Deadlift23_percent_increase'] = df['Deadlift23_percent_increase'].apply(isNoIncrease)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                               Alona Vladi\n",
       "Sex                                          F\n",
       "Event                                      SBD\n",
       "Equipment                                  Raw\n",
       "Age                                       33.0\n",
       "AgeClass                                 24-34\n",
       "BirthYearClass                           24-39\n",
       "Division                                     O\n",
       "BodyweightKg                              58.3\n",
       "WeightClassKg                               59\n",
       "Squat1Kg                                  75.0\n",
       "Squat2Kg                                  80.0\n",
       "Squat3Kg                                 -90.0\n",
       "Squat4Kg                                   NaN\n",
       "Best3SquatKg                              80.0\n",
       "Bench1Kg                                  50.0\n",
       "Bench2Kg                                  55.0\n",
       "Bench3Kg                                  60.0\n",
       "Bench4Kg                                   NaN\n",
       "Best3BenchKg                              60.0\n",
       "Deadlift1Kg                               95.0\n",
       "Deadlift2Kg                              105.0\n",
       "Deadlift3Kg                              107.5\n",
       "Deadlift4Kg                                NaN\n",
       "Best3DeadliftKg                          107.5\n",
       "TotalKg                                  247.5\n",
       "Place                                        1\n",
       "Dots                                    279.44\n",
       "Tested                                     Yes\n",
       "Country                                 Russia\n",
       "State                                      NaN\n",
       "Federation                                 GFP\n",
       "ParentFederation                           NaN\n",
       "Date                                2019-05-11\n",
       "MeetCountry                             Russia\n",
       "MeetState                                  NaN\n",
       "MeetTown                               Bryansk\n",
       "MeetName                       Open Tournament\n",
       "squat12_percent_increase              6.666667\n",
       "squat23_percent_increase                  12.5\n",
       "bench12_percent_increase                  10.0\n",
       "bench23_percent_increase              9.090909\n",
       "Deadlift12_percent_increase          10.526316\n",
       "Deadlift23_percent_increase           2.380952\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning up extreme/not so extreme outliers\n",
    "\n",
    "We have a ton of anomalies when it comes to outliers as well, we believe this to be a result of the inconsistent data entry methods that happen across powerlifting competitions. As such, there are lifters with 100+% increases which is just not a good idea at all in a sport like powerlifting, and there are lifters with negative attempt % increases which shouldn't be allowed and could be a result of earlier days of powerlifting when the \"no going down in weight\" rule wasn't in place.\n",
    "\n",
    "We will use the common filter of 1.5 * IQR to get rid of these rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_outliers(df_toclean, col):\n",
    "\n",
    "    #under no circumstances would anyone need a 100% increase\n",
    "    df_toclean = df_toclean[df_toclean[col] < 100]\n",
    "\n",
    "    #not possible to decrease weight, data entry error.\n",
    "    df_toclean = df_toclean[df_toclean[col] > 0]\n",
    "\n",
    "    Q1 = df_toclean[col].quantile(0.25)\n",
    "    Q3 = df_toclean[col].quantile(0.75)\n",
    "    IQR = Q3 - Q1\n",
    "    \n",
    "    upper_whisker = Q3 + (1.5 * IQR)\n",
    "    # Filter out the data points that are greater than the upper whisker value\n",
    "    df_toclean = df_toclean[df_toclean[col] <= upper_whisker]\n",
    "\n",
    "    return df_toclean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>squat12_percent_increase</th>\n",
       "      <th>squat23_percent_increase</th>\n",
       "      <th>bench12_percent_increase</th>\n",
       "      <th>bench23_percent_increase</th>\n",
       "      <th>Deadlift12_percent_increase</th>\n",
       "      <th>Deadlift23_percent_increase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>365540.000000</td>\n",
       "      <td>365540.000000</td>\n",
       "      <td>365540.000000</td>\n",
       "      <td>365540.000000</td>\n",
       "      <td>365540.000000</td>\n",
       "      <td>365540.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.266517</td>\n",
       "      <td>4.500666</td>\n",
       "      <td>6.503309</td>\n",
       "      <td>4.495437</td>\n",
       "      <td>6.490803</td>\n",
       "      <td>4.536791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.117106</td>\n",
       "      <td>1.859871</td>\n",
       "      <td>2.385232</td>\n",
       "      <td>1.975026</td>\n",
       "      <td>2.127265</td>\n",
       "      <td>1.878748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.010754</td>\n",
       "      <td>0.156250</td>\n",
       "      <td>0.302731</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>0.212766</td>\n",
       "      <td>0.144928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.819277</td>\n",
       "      <td>3.125000</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>3.125000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.174603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.024096</td>\n",
       "      <td>4.347826</td>\n",
       "      <td>6.250000</td>\n",
       "      <td>4.166667</td>\n",
       "      <td>6.250000</td>\n",
       "      <td>4.347826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.500000</td>\n",
       "      <td>5.617978</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>5.555556</td>\n",
       "      <td>7.843137</td>\n",
       "      <td>5.747126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>12.947658</td>\n",
       "      <td>10.013333</td>\n",
       "      <td>13.677812</td>\n",
       "      <td>10.937500</td>\n",
       "      <td>12.608696</td>\n",
       "      <td>9.865471</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       squat12_percent_increase  squat23_percent_increase  \\\n",
       "count             365540.000000             365540.000000   \n",
       "mean                   6.266517                  4.500666   \n",
       "std                    2.117106                  1.859871   \n",
       "min                    0.010754                  0.156250   \n",
       "25%                    4.819277                  3.125000   \n",
       "50%                    6.024096                  4.347826   \n",
       "75%                    7.500000                  5.617978   \n",
       "max                   12.947658                 10.013333   \n",
       "\n",
       "       bench12_percent_increase  bench23_percent_increase  \\\n",
       "count             365540.000000             365540.000000   \n",
       "mean                   6.503309                  4.495437   \n",
       "std                    2.385232                  1.975026   \n",
       "min                    0.302731                  0.095238   \n",
       "25%                    4.761905                  3.125000   \n",
       "50%                    6.250000                  4.166667   \n",
       "75%                    8.000000                  5.555556   \n",
       "max                   13.677812                 10.937500   \n",
       "\n",
       "       Deadlift12_percent_increase  Deadlift23_percent_increase  \n",
       "count                365540.000000                365540.000000  \n",
       "mean                      6.490803                     4.536791  \n",
       "std                       2.127265                     1.878748  \n",
       "min                       0.212766                     0.144928  \n",
       "25%                       5.000000                     3.174603  \n",
       "50%                       6.250000                     4.347826  \n",
       "75%                       7.843137                     5.747126  \n",
       "max                      12.608696                     9.865471  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LIFT_INCREASE_COLUMNS = ['squat12_percent_increase','squat23_percent_increase', 'bench12_percent_increase', 'bench23_percent_increase', 'Deadlift12_percent_increase', 'Deadlift23_percent_increase']\n",
    "\n",
    "df = clean_outliers(df, 'bench23_percent_increase')\n",
    "df = clean_outliers(df, 'squat12_percent_increase')\n",
    "df = clean_outliers(df, 'squat23_percent_increase')\n",
    "df = clean_outliers(df, 'bench12_percent_increase')\n",
    "df = clean_outliers(df, 'Deadlift12_percent_increase')\n",
    "df = clean_outliers(df, 'Deadlift23_percent_increase')\n",
    "\n",
    "df[LIFT_INCREASE_COLUMNS].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results\n",
    "\n",
    "## Exploratory Data Analysis\n",
    "\n",
    "\n",
    "### Basic Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#PUT REGRESSION AND T-TEST IMPORTS HERE\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "LIFT_INCREASE_COLUMNS = ['squat12_percent_increase','squat23_percent_increase', 'bench12_percent_increase', \n",
    "                        'bench23_percent_increase', 'Deadlift12_percent_increase', 'Deadlift23_percent_increase']\n",
    "palette = sns.color_palette(\"pastel\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Event</th>\n",
       "      <th>Equipment</th>\n",
       "      <th>Age</th>\n",
       "      <th>AgeClass</th>\n",
       "      <th>BirthYearClass</th>\n",
       "      <th>Division</th>\n",
       "      <th>BodyweightKg</th>\n",
       "      <th>WeightClassKg</th>\n",
       "      <th>...</th>\n",
       "      <th>MeetCountry</th>\n",
       "      <th>MeetState</th>\n",
       "      <th>MeetTown</th>\n",
       "      <th>MeetName</th>\n",
       "      <th>squat12_percent_increase</th>\n",
       "      <th>squat23_percent_increase</th>\n",
       "      <th>bench12_percent_increase</th>\n",
       "      <th>bench23_percent_increase</th>\n",
       "      <th>Deadlift12_percent_increase</th>\n",
       "      <th>Deadlift23_percent_increase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Galina Solovyanova</td>\n",
       "      <td>F</td>\n",
       "      <td>SBD</td>\n",
       "      <td>Raw</td>\n",
       "      <td>43.0</td>\n",
       "      <td>40-44</td>\n",
       "      <td>40-49</td>\n",
       "      <td>M1</td>\n",
       "      <td>73.10</td>\n",
       "      <td>74</td>\n",
       "      <td>...</td>\n",
       "      <td>Russia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bryansk</td>\n",
       "      <td>Open Tournament</td>\n",
       "      <td>5.263158</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.407407</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.090909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Aleksey Krasov</td>\n",
       "      <td>M</td>\n",
       "      <td>SBD</td>\n",
       "      <td>Raw</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35-39</td>\n",
       "      <td>24-39</td>\n",
       "      <td>O</td>\n",
       "      <td>66.65</td>\n",
       "      <td>74</td>\n",
       "      <td>...</td>\n",
       "      <td>Russia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bryansk</td>\n",
       "      <td>Open Tournament</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>4.166667</td>\n",
       "      <td>6.521739</td>\n",
       "      <td>4.081633</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3.030303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Ruslan Gasanov</td>\n",
       "      <td>M</td>\n",
       "      <td>SBD</td>\n",
       "      <td>Raw</td>\n",
       "      <td>31.5</td>\n",
       "      <td>24-34</td>\n",
       "      <td>24-39</td>\n",
       "      <td>O</td>\n",
       "      <td>102.55</td>\n",
       "      <td>105</td>\n",
       "      <td>...</td>\n",
       "      <td>Russia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bryansk</td>\n",
       "      <td>Open Tournament</td>\n",
       "      <td>7.142857</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>3.125000</td>\n",
       "      <td>8.333333</td>\n",
       "      <td>3.846154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Yuriy Solovyev</td>\n",
       "      <td>M</td>\n",
       "      <td>SBD</td>\n",
       "      <td>Wraps</td>\n",
       "      <td>26.5</td>\n",
       "      <td>24-34</td>\n",
       "      <td>24-39</td>\n",
       "      <td>O</td>\n",
       "      <td>98.90</td>\n",
       "      <td>105</td>\n",
       "      <td>...</td>\n",
       "      <td>Russia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bryansk</td>\n",
       "      <td>Open Tournament</td>\n",
       "      <td>11.111111</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.142857</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.263158</td>\n",
       "      <td>8.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Irina Syanova</td>\n",
       "      <td>F</td>\n",
       "      <td>SBD</td>\n",
       "      <td>Raw</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35-39</td>\n",
       "      <td>24-39</td>\n",
       "      <td>O</td>\n",
       "      <td>53.60</td>\n",
       "      <td>59</td>\n",
       "      <td>...</td>\n",
       "      <td>Russia</td>\n",
       "      <td>ORL</td>\n",
       "      <td>Oryol</td>\n",
       "      <td>Fortior est ferrum Tournament</td>\n",
       "      <td>7.142857</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>5.263158</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9.375000</td>\n",
       "      <td>5.714286</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Name Sex Event Equipment   Age AgeClass BirthYearClass  \\\n",
       "1   Galina Solovyanova   F   SBD       Raw  43.0    40-44          40-49   \n",
       "3       Aleksey Krasov   M   SBD       Raw  35.0    35-39          24-39   \n",
       "8       Ruslan Gasanov   M   SBD       Raw  31.5    24-34          24-39   \n",
       "12      Yuriy Solovyev   M   SBD     Wraps  26.5    24-34          24-39   \n",
       "70       Irina Syanova   F   SBD       Raw  35.0    35-39          24-39   \n",
       "\n",
       "   Division  BodyweightKg WeightClassKg  ...  MeetCountry  MeetState  \\\n",
       "1        M1         73.10            74  ...       Russia        NaN   \n",
       "3         O         66.65            74  ...       Russia        NaN   \n",
       "8         O        102.55           105  ...       Russia        NaN   \n",
       "12        O         98.90           105  ...       Russia        NaN   \n",
       "70        O         53.60            59  ...       Russia        ORL   \n",
       "\n",
       "    MeetTown                       MeetName  squat12_percent_increase  \\\n",
       "1    Bryansk                Open Tournament                  5.263158   \n",
       "3    Bryansk                Open Tournament                  5.600000   \n",
       "8    Bryansk                Open Tournament                  7.142857   \n",
       "12   Bryansk                Open Tournament                 11.111111   \n",
       "70     Oryol  Fortior est ferrum Tournament                  7.142857   \n",
       "\n",
       "    squat23_percent_increase  bench12_percent_increase  \\\n",
       "1                   5.000000                  8.000000   \n",
       "3                   4.166667                  6.521739   \n",
       "8                   3.333333                  6.666667   \n",
       "12                  5.000000                  7.142857   \n",
       "70                  6.666667                  5.263158   \n",
       "\n",
       "    bench23_percent_increase  Deadlift12_percent_increase  \\\n",
       "1                   7.407407                    10.000000   \n",
       "3                   4.081633                    10.000000   \n",
       "8                   3.125000                     8.333333   \n",
       "12                  5.000000                     5.263158   \n",
       "70                  5.000000                     9.375000   \n",
       "\n",
       "    Deadlift23_percent_increase  \n",
       "1                      9.090909  \n",
       "3                      3.030303  \n",
       "8                      3.846154  \n",
       "12                     8.750000  \n",
       "70                     5.714286  \n",
       "\n",
       "[5 rows x 44 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>squat12_percent_increase</th>\n",
       "      <th>squat23_percent_increase</th>\n",
       "      <th>bench12_percent_increase</th>\n",
       "      <th>bench23_percent_increase</th>\n",
       "      <th>Deadlift12_percent_increase</th>\n",
       "      <th>Deadlift23_percent_increase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>365540.000000</td>\n",
       "      <td>365540.000000</td>\n",
       "      <td>365540.000000</td>\n",
       "      <td>365540.000000</td>\n",
       "      <td>365540.000000</td>\n",
       "      <td>365540.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.266517</td>\n",
       "      <td>4.500666</td>\n",
       "      <td>6.503309</td>\n",
       "      <td>4.495437</td>\n",
       "      <td>6.490803</td>\n",
       "      <td>4.536791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.117106</td>\n",
       "      <td>1.859871</td>\n",
       "      <td>2.385232</td>\n",
       "      <td>1.975026</td>\n",
       "      <td>2.127265</td>\n",
       "      <td>1.878748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.010754</td>\n",
       "      <td>0.156250</td>\n",
       "      <td>0.302731</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>0.212766</td>\n",
       "      <td>0.144928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.819277</td>\n",
       "      <td>3.125000</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>3.125000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.174603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.024096</td>\n",
       "      <td>4.347826</td>\n",
       "      <td>6.250000</td>\n",
       "      <td>4.166667</td>\n",
       "      <td>6.250000</td>\n",
       "      <td>4.347826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.500000</td>\n",
       "      <td>5.617978</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>5.555556</td>\n",
       "      <td>7.843137</td>\n",
       "      <td>5.747126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>12.947658</td>\n",
       "      <td>10.013333</td>\n",
       "      <td>13.677812</td>\n",
       "      <td>10.937500</td>\n",
       "      <td>12.608696</td>\n",
       "      <td>9.865471</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       squat12_percent_increase  squat23_percent_increase  \\\n",
       "count             365540.000000             365540.000000   \n",
       "mean                   6.266517                  4.500666   \n",
       "std                    2.117106                  1.859871   \n",
       "min                    0.010754                  0.156250   \n",
       "25%                    4.819277                  3.125000   \n",
       "50%                    6.024096                  4.347826   \n",
       "75%                    7.500000                  5.617978   \n",
       "max                   12.947658                 10.013333   \n",
       "\n",
       "       bench12_percent_increase  bench23_percent_increase  \\\n",
       "count             365540.000000             365540.000000   \n",
       "mean                   6.503309                  4.495437   \n",
       "std                    2.385232                  1.975026   \n",
       "min                    0.302731                  0.095238   \n",
       "25%                    4.761905                  3.125000   \n",
       "50%                    6.250000                  4.166667   \n",
       "75%                    8.000000                  5.555556   \n",
       "max                   13.677812                 10.937500   \n",
       "\n",
       "       Deadlift12_percent_increase  Deadlift23_percent_increase  \n",
       "count                365540.000000                365540.000000  \n",
       "mean                      6.490803                     4.536791  \n",
       "std                       2.127265                     1.878748  \n",
       "min                       0.212766                     0.144928  \n",
       "25%                       5.000000                     3.174603  \n",
       "50%                       6.250000                     4.347826  \n",
       "75%                       7.843137                     5.747126  \n",
       "max                      12.608696                     9.865471  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[LIFT_INCREASE_COLUMNS].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion\n",
    "\n",
    "Looks like we lost a lot of data when taking outliers out (from 3043013 to 365540), this was unfortunately expected when working with a sport where methods of tracking scores are prone to errors in human input, and where data input isn't strongly supervised. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gender/Weight Class Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Remove duplicate names/sex, get the number of times they've competed, and then count the sexes\n",
    "df_times_competed = df.groupby(['Name','Sex']).size().reset_index(name='times_competed')\n",
    "df_times_competed['Sex'].value_counts()\n",
    "\n",
    "sns.countplot(data=df, x='Sex', color='#258fdf')\n",
    "plt.title('Gender Distribution', fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Powerlifting has a majority Male audience and it shows here, with little to no Mx lifters as only rarely do they get their own category in federations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "WEIGHT_CLASS_ORDER = ['43', '47', '53', '59', '66', '74', '83', '93', '105', '120', '120+']\n",
    "\n",
    "weight_class_chart_font_size = 16\n",
    "plt.figure(figsize=(20,5))\n",
    "plt.title(\"Distribution of Weight Classes, seperated by Sex\", fontsize = weight_class_chart_font_size)\n",
    "plt.xticks(fontsize = weight_class_chart_font_size)\n",
    "plt.yticks(fontsize = weight_class_chart_font_size)\n",
    "plt.xlabel(xlabel=\"Weight Class (kg)\", fontsize=weight_class_chart_font_size)\n",
    "plt.ylabel(ylabel=\"Count\", fontsize=weight_class_chart_font_size)\n",
    "\n",
    "sns.countplot(data=df, x='WeightClassKg', hue='Sex', order=WEIGHT_CLASS_ORDER, linewidth=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems that women are more common in the lighter weight classes, and are more evenly distributed across the weight classes. Meanwhile, men tend to hover around mainly the 74-105kg weight classes. There are very few competitors in the lighter weight classes (43-47kg), so we mayu not have enough data to come to any conclusions in regards to those weight classes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Effects of Sex on % attempt increases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Jumps between Attempts 1 and 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "BINS_INCREASE_RANGE = [n * 1 for n in range(30)]\n",
    "HIST_TICKS = [n * 5 for n in range(7)]\n",
    "plt.figure(figsize=(20,5))\n",
    "\n",
    "xp = 'squat12_percent_increase'\n",
    "plt.subplot(1, 3, 1)\n",
    "sns.histplot(data=df, x=xp, bins=BINS_INCREASE_RANGE, hue='Sex')\n",
    "plt.xticks(ticks=HIST_TICKS)\n",
    "plt.title('Percent increase between Squat 1 and Squat 2', fontsize=14)    \n",
    "plt.xlabel(\"% increase in weight between attempts\")\n",
    "\n",
    "\n",
    "xp = 'bench12_percent_increase'\n",
    "plt.subplot(1, 3, 2)\n",
    "sns.histplot(data=df, x=xp, bins=BINS_INCREASE_RANGE, hue='Sex')\n",
    "plt.title('Percent increase between Bench 1 and Bench 2', fontsize=14)       \n",
    "plt.xticks(ticks=HIST_TICKS)\n",
    "plt.xlabel(\"% increase in weight between attempts\")\n",
    "\n",
    "\n",
    "xp = 'Deadlift12_percent_increase'\n",
    "plt.subplot(1,3,3)\n",
    "sns.histplot(data=df,x=xp, bins=BINS_INCREASE_RANGE, hue='Sex')\n",
    "plt.xticks(ticks=HIST_TICKS)\n",
    "plt.title('Percent increase between Deadlift 1 and Deadlift 2', fontsize=14) \n",
    "plt.xlabel(\"% increase in weight between attempts\")\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Jumps in Squat 1, 2\n",
    "From the graph, there seems to be are a large number of men who jump between 5 percent - 7 percent between squats 1 and 2, and for women, the most common percent increase in weights seems to be around the same percentage, however, there seems to much less disparity for women as compared to men. \n",
    "\n",
    "### Jumps in Bench 1, 2\n",
    "The graph shows that the most common percent increase between bench 1 and 2 lie between 5 - 7 percent but the jumps are slightly more spread out as compared to the percent increases in weight for the squats. For women, the most common percent increase in weights seems to be more spread out, and lie between 5 and 9 percent, and there seems to be no clear peak as compared to men. \n",
    "\n",
    "### Jumps in Deadlift 1, 2\n",
    "For the deadlifts 1 and 2 percent increases, men lie between 5 and 7 percent once again, with women having a more clear peak for deadlifts, with their most common percent increases lying in between 6 and 8 percent. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Jumps between Attempt 2 and 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5))\n",
    "\n",
    "xp = 'squat23_percent_increase'\n",
    "plt.subplot(1, 3, 1)\n",
    "sns.histplot(data=df, x=xp, bins=BINS_INCREASE_RANGE, hue='Sex')\n",
    "plt.xticks(ticks=HIST_TICKS)\n",
    "plt.title('Percent increase between Squat 2 and Squat 3', fontsize=14)\n",
    "plt.xlabel(\"% increase in weight between attempts\")\n",
    "\n",
    "\n",
    "xp = 'bench23_percent_increase'\n",
    "plt.subplot(1, 3, 2)\n",
    "sns.histplot(data=df, x=xp, bins=BINS_INCREASE_RANGE, hue='Sex')\n",
    "plt.xticks(ticks=HIST_TICKS)\n",
    "plt.title('Percent increase between Bench 2 and Bench 3', fontsize=14)\n",
    "plt.xlabel(\"% increase in weight between attempts\")\n",
    "\n",
    "xp = 'Deadlift23_percent_increase'\n",
    "plt.subplot(1,3,3)\n",
    "sns.histplot(data=df,x=xp, bins=BINS_INCREASE_RANGE, hue='Sex')\n",
    "plt.xticks(ticks=HIST_TICKS)\n",
    "plt.title('Percent increase between Deadlift 2 and Deadlift 3', fontsize=14)\n",
    "plt.xlabel(\"% increase in weight between attempts\")\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Jumps in Squat 2, 3\n",
    "The percent increase between squats 2 and 3 are expected to be slightly lesser than the increases between 1 and 2, and the graph shows this. The most common percent increases in weight in this case are between 3 and 6 percent, with most being near 3-5 percent increases. Women have also slightly reduced their percent increases as they lie primarily between 4 and 6 percent.\n",
    "\n",
    "### Jumps in Bench 2, 3\n",
    "The difference in percent increases are also slightly lesser for the bench 2 and 3 attempts. The most common percent increases are between 3-5 percent increases, while women are majorly between 4-6 percent with most being in between 4-5 percent, which is a big change from 5-9 percent between attempts 1 and 2. \n",
    "\n",
    "### Jumps in Deadlift 2, 3\n",
    "The men had a common percent increase of 3-6 percent, with most being from 3-5 percent and this is again a smaller percent increase as compared to attmpts 1 and 2. For the women, the most common percent increase is between 4-6 percent and this is also a smaller jump compared to 6-8 percent between attempts 1 and 2. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Closer Look at Effect of Sex on % Increase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define your figure size\n",
    "plt.figure(figsize=(12, 6))\n",
    "min_value = 0\n",
    "max_value = 15\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "sns.boxplot(data=df, y='squat12_percent_increase', hue=\"Sex\", dodge=True, gap=0.1)\n",
    "plt.ylim(min_value, max_value)\n",
    "plt.ylabel(ylabel=\"Percent Increase between Squat 1 and Squat 2\")\n",
    "plt.title('Percent increase between Squat 1 and Squat 2 By Sex', fontsize=12)\n",
    "plt.legend(loc='upper right')\n",
    "\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "sns.boxplot(data =df, y='squat23_percent_increase', hue=\"Sex\", dodge=True, gap=0.1)\n",
    "plt.ylabel(ylabel=\"Percent Increase between Squat 2 and Squat 3\")\n",
    "plt.ylim(min_value, max_value)\n",
    "plt.title('Percent increase between Squat 2 and Squat 3 by Sex', fontsize=12)\n",
    "plt.legend(loc='upper right')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion\n",
    "There seems to be very small differences between men and women when it comes to the Squat, but the median is overall higher for Women in the Squat. There also seems to be a lot more outliers when it comes to men, which might suggest they more often take extreme jumps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "min_value = 0\n",
    "max_value = 15\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "sns.boxplot(data=df, y='bench12_percent_increase', hue=\"Sex\", dodge=True, gap=0.1)\n",
    "plt.ylim(min_value, max_value)\n",
    "plt.ylabel(ylabel=\"Percent Increase between Bench 1 and Bench 2\")\n",
    "plt.title('Percent increase between Bench 1 and Bench 2 by Sex', fontsize=12)\n",
    "\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "sns.boxplot(data =df, y='bench23_percent_increase', hue=\"Sex\", dodge=True, gap=0.1)\n",
    "plt.ylim(min_value, max_value)\n",
    "plt.ylabel(ylabel=\"Percent Increase between Bench 2 and Bench 3\")\n",
    "plt.title('Percent increase between Bench 2 and Bench 3 by Sex', fontsize=12)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion\n",
    "From these two graphs, our hypothesis that men might take higher jumps in the Bench Press may seem flawed. It looks like the median for women is almost a percent higher than it is for men especially in the 3rd quartile, so on the extreme end of jumps, women seem to take bigger jumps in the bench press."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "min_value = 0\n",
    "max_value = 15\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "sns.boxplot(data=df, y='Deadlift12_percent_increase', hue=\"Sex\", dodge=True, gap=0.1)\n",
    "plt.ylim(min_value, max_value)\n",
    "plt.ylabel(ylabel=\"Percent Increase between Deadlift 1 and Deadlift 2\")\n",
    "plt.title('Percent increase between Deadlift 1 and Deadlift 2 By Sex', fontsize=12)\n",
    "\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "sns.boxplot(data =df, y='Deadlift23_percent_increase', hue=\"Sex\", dodge=True, gap=0.1)\n",
    "plt.ylim(min_value, max_value)\n",
    "plt.ylabel(ylabel=\"Percent Increase between Deadlift 2 and Deadlift 3\")\n",
    "plt.title('Percent increase between Deadlift 2 and Deadlift 3 By Sex', fontsize=12)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion\n",
    "The same trend is seen here like in the last visualization for Bench press, where women are making slightly higher jumps, but it is not very significant, probably a 0.5% difference. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A look at effect of Weight Class on % Increase\n",
    "\n",
    "Now lets see if we can see any relationship between weight class and attempt selection, we will hold off on any discussion until after the three visualizations as they all follow the same trend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "sns.boxplot(data=df, x='squat12_percent_increase', hue=\"WeightClassKg\", dodge=True, gap=0.1, hue_order = WEIGHT_CLASS_ORDER)\n",
    "plt.legend(loc='upper left')\n",
    "plt.title('Percent increase between Squat 1 and Squat 2 By Weight Class', fontsize=10)    \n",
    "plt.xlabel(xlabel=\"Percent Increase between Squat 1 and Squat 2\")\n",
    "plt.xlim(0,15)\n",
    "\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "sns.boxplot(data=df, x='squat23_percent_increase', hue=\"WeightClassKg\", dodge=True, gap=0.1, hue_order = WEIGHT_CLASS_ORDER)\n",
    "plt.legend(loc='upper left')\n",
    "plt.title('Percent increase between Squat 2 and Squat 3 By Weight Class', fontsize=10)  \n",
    "plt.xlabel(xlabel=\"Percent Increase between Squat 2 and Squat 3\")\n",
    "plt.xlim(0,15)\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "sns.boxplot(data=df, x='bench12_percent_increase', hue=\"WeightClassKg\", dodge=True, gap=0.1, hue_order = WEIGHT_CLASS_ORDER)\n",
    "plt.title('Percent increase between Bench 1 and Bench 2 By Weight Class', fontsize=10)\n",
    "plt.xlabel(xlabel=\"Percent Increase between Bench 1 and Bench 2\")\n",
    "plt.xlim(0,15)\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "sns.boxplot(data=df, x='bench23_percent_increase', hue=\"WeightClassKg\", dodge=True, gap=0.1, hue_order = WEIGHT_CLASS_ORDER)\n",
    "plt.title('Percent increase between Bench 2 and Bench 3 By Weight Class', fontsize=10)\n",
    "plt.xlabel(xlabel=\"Percent Increase between Bench 1 and Bench 2\")\n",
    "plt.xlim(0,15)\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "sns.boxplot(data=df, x='Deadlift12_percent_increase', hue=\"WeightClassKg\", dodge=True, gap=0.1, hue_order = WEIGHT_CLASS_ORDER)\n",
    "plt.ylabel(\"Weight Class\")\n",
    "plt.title('Percent increase between Deadlift 1 and Deadlift 2 By Weight Class', fontsize=10)\n",
    "plt.xlabel(xlabel=\"Percent Increase between Deadlift 1 and Deadlift 2\")\n",
    "plt.xlim(0,15)\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "sns.boxplot(data=df, x='Deadlift23_percent_increase', hue=\"WeightClassKg\", dodge=True, gap=0.1, hue_order = WEIGHT_CLASS_ORDER)\n",
    "plt.ylabel(\"Weight Class\")\n",
    "plt.xlabel(xlabel=\"Percent Increase between Deadlift 2 and Deadlift 3\")\n",
    "plt.title('Percent increase between Deadlift 2 and Deadlift 3 By Weight Class', fontsize=10)\n",
    "plt.xlim(0,15)\n",
    "\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion on Weight Class\n",
    "\n",
    "Between attempt 1 and attempt 2, weight classes usually hovered around 6% jumps, and between the second and third attempts around 4%. There was a downward trend in the median when looking across weight classes, but only around a 0.5% decrease when going from the 43 kg weight class to the 120+ weight class, so per kilo there doesn't appear to be a huge effect on the attempt selection across all the lifts.\n",
    "\n",
    "There seems to be a sharp decline from the 43 kg weight class to the 47 kg weight class, but we believe there is just not enough data (< 500 datapoints) from the 43 kg weight class to accurately say anything from this visual inference. The bias could cause this difference. Around the middle weight classes (74-93kg) which we have the most data from according to the weight class distribution, there seems to be only about a 0.1% difference in attempt selection between weight classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "M     56173\n",
       "F      7499\n",
       "Mx        5\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['WeightClassKg'] == '93']['Sex'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis - Little Extra Data Wrangling and importing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "import patsy\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def retrieveEventFromJumpLabel(jump_label):\n",
    "    if \"squat\" in jump_label:\n",
    "        return \"SQUAT\"\n",
    "    elif \"Deadlift\" in jump_label:\n",
    "        return \"DEADLIFT\"\n",
    "    elif \"bench\" in jump_label:\n",
    "        return \"BENCH\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>BodyweightKg</th>\n",
       "      <th>WeightClassKg</th>\n",
       "      <th>EventJump</th>\n",
       "      <th>PercentJump</th>\n",
       "      <th>Event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Galina Solovyanova</td>\n",
       "      <td>F</td>\n",
       "      <td>73.10</td>\n",
       "      <td>74</td>\n",
       "      <td>squat12_percent_increase</td>\n",
       "      <td>5.263158</td>\n",
       "      <td>SQUAT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aleksey Krasov</td>\n",
       "      <td>M</td>\n",
       "      <td>66.65</td>\n",
       "      <td>74</td>\n",
       "      <td>squat12_percent_increase</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>SQUAT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ruslan Gasanov</td>\n",
       "      <td>M</td>\n",
       "      <td>102.55</td>\n",
       "      <td>105</td>\n",
       "      <td>squat12_percent_increase</td>\n",
       "      <td>7.142857</td>\n",
       "      <td>SQUAT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Yuriy Solovyev</td>\n",
       "      <td>M</td>\n",
       "      <td>98.90</td>\n",
       "      <td>105</td>\n",
       "      <td>squat12_percent_increase</td>\n",
       "      <td>11.111111</td>\n",
       "      <td>SQUAT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Irina Syanova</td>\n",
       "      <td>F</td>\n",
       "      <td>53.60</td>\n",
       "      <td>59</td>\n",
       "      <td>squat12_percent_increase</td>\n",
       "      <td>7.142857</td>\n",
       "      <td>SQUAT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2193235</th>\n",
       "      <td>Lucy Cohen</td>\n",
       "      <td>F</td>\n",
       "      <td>70.83</td>\n",
       "      <td>74</td>\n",
       "      <td>Deadlift23_percent_increase</td>\n",
       "      <td>5.263158</td>\n",
       "      <td>DEADLIFT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2193236</th>\n",
       "      <td>Tetyana Melnyk</td>\n",
       "      <td>F</td>\n",
       "      <td>72.15</td>\n",
       "      <td>74</td>\n",
       "      <td>Deadlift23_percent_increase</td>\n",
       "      <td>2.469136</td>\n",
       "      <td>DEADLIFT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2193237</th>\n",
       "      <td>Nadezhda Sindikas</td>\n",
       "      <td>F</td>\n",
       "      <td>80.81</td>\n",
       "      <td>83</td>\n",
       "      <td>Deadlift23_percent_increase</td>\n",
       "      <td>4.878049</td>\n",
       "      <td>DEADLIFT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2193238</th>\n",
       "      <td>Frida Wik</td>\n",
       "      <td>F</td>\n",
       "      <td>75.98</td>\n",
       "      <td>83</td>\n",
       "      <td>Deadlift23_percent_increase</td>\n",
       "      <td>6.410256</td>\n",
       "      <td>DEADLIFT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2193239</th>\n",
       "      <td>Bonica Brown</td>\n",
       "      <td>F</td>\n",
       "      <td>128.58</td>\n",
       "      <td>120+</td>\n",
       "      <td>Deadlift23_percent_increase</td>\n",
       "      <td>1.098901</td>\n",
       "      <td>DEADLIFT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2193240 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Name Sex  BodyweightKg WeightClassKg  \\\n",
       "0        Galina Solovyanova   F         73.10            74   \n",
       "1            Aleksey Krasov   M         66.65            74   \n",
       "2            Ruslan Gasanov   M        102.55           105   \n",
       "3            Yuriy Solovyev   M         98.90           105   \n",
       "4             Irina Syanova   F         53.60            59   \n",
       "...                     ...  ..           ...           ...   \n",
       "2193235          Lucy Cohen   F         70.83            74   \n",
       "2193236      Tetyana Melnyk   F         72.15            74   \n",
       "2193237   Nadezhda Sindikas   F         80.81            83   \n",
       "2193238           Frida Wik   F         75.98            83   \n",
       "2193239        Bonica Brown   F        128.58          120+   \n",
       "\n",
       "                           EventJump  PercentJump     Event  \n",
       "0           squat12_percent_increase     5.263158     SQUAT  \n",
       "1           squat12_percent_increase     5.600000     SQUAT  \n",
       "2           squat12_percent_increase     7.142857     SQUAT  \n",
       "3           squat12_percent_increase    11.111111     SQUAT  \n",
       "4           squat12_percent_increase     7.142857     SQUAT  \n",
       "...                              ...          ...       ...  \n",
       "2193235  Deadlift23_percent_increase     5.263158  DEADLIFT  \n",
       "2193236  Deadlift23_percent_increase     2.469136  DEADLIFT  \n",
       "2193237  Deadlift23_percent_increase     4.878049  DEADLIFT  \n",
       "2193238  Deadlift23_percent_increase     6.410256  DEADLIFT  \n",
       "2193239  Deadlift23_percent_increase     1.098901  DEADLIFT  \n",
       "\n",
       "[2193240 rows x 7 columns]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysis_df = pd.melt(df, id_vars=[\"Name\",\"Sex\",\"BodyweightKg\", \"WeightClassKg\"], value_vars=LIFT_INCREASE_COLUMNS, var_name='EventJump', value_name='PercentJump')\n",
    "analysis_df['Event'] = analysis_df[\"EventJump\"].apply(retrieveEventFromJumpLabel)\n",
    "\n",
    "analysis_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis - Mann-Whitney U test\n",
    "\n",
    "We saw a bunch of visualizations, comparing medians by sex, weight class, etc. Lets see if theres anything significant going on by using a t-test. Lets do some setup to reduce the amount of code we have to write in this section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "from scipy.stats import mannwhitneyu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section to compare male and female bodyweights using a Mann-Whitney U test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Given a column, run a Mann-Whitney U test on the column, two distributions will be made by seperating those belonging to Males and Females and \n",
    "#compared using the Mann-Whitney U test\n",
    "# adjust the alternative hypothesis to test if the first variable is greater than the second variable (1-sided test)\n",
    "def mannwhitneyu_column_by_sex(df, col):\n",
    "    male_col = df[df['Sex'] == \"M\"][col]\n",
    "    female_col = df[df['Sex'] == \"F\"][col]\n",
    "\n",
    "    male_col = male_col[~male_col.isna()]\n",
    "    female_col = female_col[~female_col.isna()]\n",
    "\n",
    "    return mannwhitneyu(male_col,female_col,alternative='greater')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.569126e+06\n",
       "mean     9.066118e+01\n",
       "std      1.919543e+01\n",
       "min      2.340000e+01\n",
       "25%      7.825000e+01\n",
       "50%      8.890000e+01\n",
       "75%      1.012000e+02\n",
       "max      2.850000e+02\n",
       "Name: BodyweightKg, dtype: float64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysis_df[(analysis_df['Sex'] == 'M') & (~analysis_df['BodyweightKg'].isna())]['BodyweightKg'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    622980.000000\n",
       "mean         70.345191\n",
       "std          16.871617\n",
       "min          22.600000\n",
       "25%          59.000000\n",
       "50%          67.145000\n",
       "75%          77.950000\n",
       "max         188.100000\n",
       "Name: BodyweightKg, dtype: float64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysis_df[(analysis_df['Sex'] == 'F') & (~analysis_df['BodyweightKg'].isna())]['BodyweightKg'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because the weights for males and females do not have a normal distribution, we will be opting to use the nonparametric stats test of Mann-Whitney U."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code to run Mann-Whitney U test to compare Male vs. Female Bodyweight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=793918611846.0, pvalue=0.0)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitneyu_column_by_sex(analysis_df, \"BodyweightKg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this result, we see that the p-value is approximately 0. Therefore, we reject the null hypothesis given that there is convincing evidence that the mean of males' bodyweights is greater than the mean of females' bodyweights.\n",
    "\n",
    "Additionally, to support this test's results, we can also look at the means and see that the mean male bodyweight is 90 kg and the female bodyweight is 70kg, thus further establishing that this difference of about 20kg is statistically significant."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section to compare 43kg to 120+kg weight classes' PercentJump using Mann-Whitney U test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Given two weight classes, run a Mann-Whitney U test on a specified column of the df.\n",
    "def mannwhitneyu_column_by_weight_class(df, weightclass1,weightclass2, col):\n",
    "    weightclass1_col = df[df['WeightClassKg'] == weightclass1][col]\n",
    "    weightclass2_col = df[df['WeightClassKg'] == weightclass2][col]\n",
    "\n",
    "    weightclass1_col = weightclass1_col[~weightclass1_col.isna()]\n",
    "    weightclass2_col = weightclass2_col[~weightclass2_col.isna()]\n",
    "\n",
    "    return mannwhitneyu(weightclass1_col,weightclass2_col, alternative = 'greater')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because the weightclasses' PercentJump does not have a normal distribution, we will be opting to use the nonparametric stats test of Mann-Whitney U."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code to run Mann-Whitney U test to compare PercentJump for the 43kg and 120kg+ Bodyweight class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=225961907.0, pvalue=1.1872199872198982e-176)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitneyu_column_by_weight_class(analysis_df, '43','120+', 'PercentJump')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this result, we can see that the p-value is 1.187e-176, which is approximately 0. Therefore, we reject the null hypothesis as there is convincing evidence that the percent jump of the 43kg+ weight class is larger than the percent jump of the 120kg+ weight class.\n",
    "\n",
    "We can also look at the means and see that the 43kg weight class has a mean percent jump of 6.7% and the 120kg+ weight class has a mean percent jump of 5.3%, thus further establishing that the 43 kg weight class has a higher percent jump than the 120kg+ weight class."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis - Regression\n",
    "\n",
    "We can finally run some regressions to see how much of a relationship these different factors like Sex and Event have on the lifter's attempt jumps! Lets check out the factors talks about in our first hypothesis, does Sex impact how much you'll jump up in weight? If so, are Male lifters making the bigger jumps or are Female lifters making the bigger jumps?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:            PercentJump   R-squared:                       0.018\n",
      "Model:                            OLS   Adj. R-squared:                  0.018\n",
      "Method:                 Least Squares   F-statistic:                 1.985e+04\n",
      "Date:                Sun, 10 Mar 2024   Prob (F-statistic):               0.00\n",
      "Time:                        18:14:18   Log-Likelihood:            -4.8965e+06\n",
      "No. Observations:             2193240   AIC:                         9.793e+06\n",
      "Df Residuals:                 2193237   BIC:                         9.793e+06\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      5.9471      0.003   2081.014      0.000       5.941       5.953\n",
      "Sex[T.M]      -0.6727      0.003   -199.160      0.000      -0.679      -0.666\n",
      "Sex[T.Mx]      1.0030      0.255      3.926      0.000       0.502       1.504\n",
      "==============================================================================\n",
      "Omnibus:                    97295.135   Durbin-Watson:                   1.498\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           110842.056\n",
      "Skew:                           0.549   Prob(JB):                         0.00\n",
      "Kurtosis:                       3.079   Cond. No.                         211.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "outcome, predictors = patsy.dmatrices('PercentJump ~ Sex', analysis_df)\n",
    "mod = sm.OLS(outcome, predictors)  \n",
    "res = mod.fit()\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion\n",
    "\n",
    "Based off the results of this regression, about how much difference, if any, does Sex play when it comes to attempt weight selection? With p < 0.001, and a coefficient of -0.6727 compared to women, we can say that Men actually make 0.6% smaller jumps on average than women in general. This comes as a surprise as we thought that since men lift more on average, they would be more confident in taking bigger jumps. Although the p-value makes this a confident answer, it isn't a signficant amount of weight difference between men and women.\n",
    "\n",
    "Continuing on, we can filter by Lift, and then see whether maybe Men have a higher jump percentage(%) on average in a specific lift. Our second hypothesis claims that since Men's attempts at the bench press make up more of their total weight lifted than when it comes to women, they will have the confidence to make higher jumps than women. We can apply this same thinking with women and the squat, so they should have higher jumps in the squat."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Effect of Sex on Weight lifted, by Event"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Squat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:            PercentJump   R-squared:                       0.010\n",
      "Model:                            OLS   Adj. R-squared:                  0.010\n",
      "Method:                 Least Squares   F-statistic:                     3522.\n",
      "Date:                Sun, 10 Mar 2024   Prob (F-statistic):               0.00\n",
      "Time:                        18:11:01   Log-Likelihood:            -1.6034e+06\n",
      "No. Observations:              731080   AIC:                         3.207e+06\n",
      "Df Residuals:                  731077   BIC:                         3.207e+06\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      5.7211      0.005   1202.149      0.000       5.712       5.730\n",
      "Sex[T.M]      -0.4715      0.006    -83.833      0.000      -0.483      -0.461\n",
      "Sex[T.Mx]      1.3215      0.425      3.106      0.002       0.488       2.155\n",
      "==============================================================================\n",
      "Omnibus:                    29282.889   Durbin-Watson:                   1.516\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            32921.364\n",
      "Skew:                           0.516   Prob(JB):                         0.00\n",
      "Kurtosis:                       3.121   Cond. No.                         211.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "outcome, predictors = patsy.dmatrices('PercentJump ~ Sex', analysis_df[analysis_df['Event'] == \"SQUAT\"])\n",
    "mod = sm.OLS(outcome, predictors)  \n",
    "res = mod.fit()\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion\n",
    "\n",
    "This result is as expected, with a p value < 0.0001, we can say that men on average take approximately -0.5% smaller jumps than women when it comes to the squat. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bench"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:            PercentJump   R-squared:                       0.039\n",
      "Model:                            OLS   Adj. R-squared:                  0.039\n",
      "Method:                 Least Squares   F-statistic:                 1.482e+04\n",
      "Date:                Sun, 10 Mar 2024   Prob (F-statistic):               0.00\n",
      "Time:                        18:11:40   Log-Likelihood:            -1.6656e+06\n",
      "No. Observations:              731080   AIC:                         3.331e+06\n",
      "Df Residuals:                  731077   BIC:                         3.331e+06\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      6.2539      0.005   1207.008      0.000       6.244       6.264\n",
      "Sex[T.M]      -1.0541      0.006   -172.126      0.000      -1.066      -1.042\n",
      "Sex[T.Mx]      1.1018      0.463      2.379      0.017       0.194       2.010\n",
      "==============================================================================\n",
      "Omnibus:                    44662.350   Durbin-Watson:                   1.492\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            53402.822\n",
      "Skew:                           0.661   Prob(JB):                         0.00\n",
      "Kurtosis:                       3.087   Cond. No.                         211.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "outcome, predictors = patsy.dmatrices('PercentJump ~ Sex', analysis_df[analysis_df['Event'] == \"BENCH\"])\n",
    "mod = sm.OLS(outcome, predictors)  \n",
    "res = mod.fit()\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion\n",
    "\n",
    "This is the most unexpected result, and is also the result with the highest magnitude. With a p-value of < 0.0001, Men actually take approximately -1% smaller jumps than women on the bench press. One of our hypotheis was based off past work that found that the bench press made up a higher percent of a Man's total than it did for a Woman's total, so we thought they would then take higher jumps. In reality, it seems to be the inverse."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deadlift "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:            PercentJump   R-squared:                       0.010\n",
      "Model:                            OLS   Adj. R-squared:                  0.010\n",
      "Method:                 Least Squares   F-statistic:                     3658.\n",
      "Date:                Sun, 10 Mar 2024   Prob (F-statistic):               0.00\n",
      "Time:                        18:11:54   Log-Likelihood:            -1.6207e+06\n",
      "No. Observations:              731080   AIC:                         3.241e+06\n",
      "Df Residuals:                  731077   BIC:                         3.241e+06\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      5.8663      0.005   1203.863      0.000       5.857       5.876\n",
      "Sex[T.M]      -0.4925      0.006    -85.507      0.000      -0.504      -0.481\n",
      "Sex[T.Mx]      0.5856      0.436      1.344      0.179      -0.268       1.439\n",
      "==============================================================================\n",
      "Omnibus:                    20308.166   Durbin-Watson:                   1.501\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            22026.730\n",
      "Skew:                           0.424   Prob(JB):                         0.00\n",
      "Kurtosis:                       2.937   Cond. No.                         211.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "outcome, predictors = patsy.dmatrices('PercentJump ~ Sex', analysis_df[analysis_df['Event'] == \"DEADLIFT\"])\n",
    "mod = sm.OLS(outcome, predictors)  \n",
    "res = mod.fit()\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion\n",
    "\n",
    "With a significant p-value of < 0.0001,  Men take approximately -0.5% smaller jumps than women on the deadlift. This is also as expected, as the past work on how much the Deadlift makes up an athletes total by sex showed that Women's totals are made up more by their deadlift, as compared to men."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What event do people take higher jumps on?\n",
    "\n",
    "Dialing is back a bit in terms of scope for our third hypothesis: Is deadlift really the most important lift in a competition to a lifter? Do people take the highest jumps when it comes to the lift that makes up the most of their total?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:            PercentJump   R-squared:                       0.001\n",
      "Model:                            OLS   Adj. R-squared:                  0.001\n",
      "Method:                 Least Squares   F-statistic:                     719.2\n",
      "Date:                Sun, 10 Mar 2024   Prob (F-statistic):          5.53e-313\n",
      "Time:                        19:08:01   Log-Likelihood:            -4.9155e+06\n",
      "No. Observations:             2193240   AIC:                         9.831e+06\n",
      "Df Residuals:                 2193237   BIC:                         9.831e+06\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "Intercept             5.4994      0.003   2066.327      0.000       5.494       5.505\n",
      "Event[T.DEADLIFT]     0.0144      0.004      3.832      0.000       0.007       0.022\n",
      "Event[T.SQUAT]       -0.1158      0.004    -30.762      0.000      -0.123      -0.108\n",
      "==============================================================================\n",
      "Omnibus:                    99415.542   Durbin-Watson:                   1.480\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           113566.460\n",
      "Skew:                           0.556   Prob(JB):                         0.00\n",
      "Kurtosis:                       3.084   Cond. No.                         3.73\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "outcome, predictors = patsy.dmatrices('PercentJump ~ Event', analysis_df)\n",
    "mod = sm.OLS(outcome, predictors)  \n",
    "res = mod.fit()\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion\n",
    "According to our analysis, it is as expected. Using the average jump in the bench press as a baseline, the deadlift comes with approximately a 0.01% increase in the average jump, while it goes down in the Squat by about -0.11%. Our p-values suggest this is a strong result, but like previous results in this project, a ~0.1% difference between the events is not a significant different and does not really make a difference in competition."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Do heavier lifters take bigger attempt jumps?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We took a look in the EDA section, making boxplots split across the weight classes we defined, and saw that there was only about a 0.5% decrease when looking at jumps from the 40kg class to the 120kg class. Will our regression support this? Here we are dropping the use of weight classes which made visualizations easier and using the BodyweightKg column since the data is more granular than grouping everyone into ~10 different weight classes. Interpretation is also easier so we can see per kilogram in bodyweight, how much does someone's attempt increase or decrease?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:            PercentJump   R-squared:                       0.007\n",
      "Model:                            OLS   Adj. R-squared:                  0.007\n",
      "Method:                 Least Squares   F-statistic:                 1.458e+04\n",
      "Date:                Sun, 10 Mar 2024   Prob (F-statistic):               0.00\n",
      "Time:                        19:19:25   Log-Likelihood:            -4.9064e+06\n",
      "No. Observations:             2192184   AIC:                         9.813e+06\n",
      "Df Residuals:                 2192182   BIC:                         9.813e+06\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "================================================================================\n",
      "                   coef    std err          t      P>|t|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------\n",
      "Intercept        6.2243      0.006    962.491      0.000       6.212       6.237\n",
      "BodyweightKg    -0.0089    7.4e-05   -120.752      0.000      -0.009      -0.009\n",
      "==============================================================================\n",
      "Omnibus:                    99044.302   Durbin-Watson:                   1.486\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           113102.048\n",
      "Skew:                           0.555   Prob(JB):                         0.00\n",
      "Kurtosis:                       3.080   Cond. No.                         369.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "outcome, predictors = patsy.dmatrices('PercentJump ~ BodyweightKg', analysis_df)\n",
    "mod = sm.OLS(outcome, predictors)  \n",
    "res = mod.fit()\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion\n",
    "\n",
    "Does this align with what we saw on the boxplots? Lets use our intercept and BodyweightKG to find out.\n",
    "\n",
    "- A 40kg lifter would have a jump of 6.2243 * -0.0089(40) = 5.863\n",
    "- A 120kg lifter would have a jump of 6.2243 * -0.0089(120) = 5.156\n",
    "- A total difference of -0.707% jump from a 40kg lifter to a 120kg lifter\n",
    "\n",
    "This lines up with the small negative relationship we saw in the boxplots, but again, as with the rest of the results, this won't be a significant difference in the results of a competition. Remember that athletes are competing with those in their weight class, when it comes to a 70kg weight class, they only have to worry about those -10kg lighter than them (a 61kg lifter for example, just over 60kg weight class). A -0.0089 coefficient per kilogram means the 60kg opponent will make a 0.01% higher jump than that of the 70kg lifter. So although there is a small effect, it is competitively insignificant in the context of weight classes. There is also the fact that in powerlifting competitions, you will either keep the same weight for your next attempt (commonly when an attempt is failed), or go up in weight which must be at least a 2.5kg increase. Thus, a 0.01% jump when attempts are usually <= 200kg, is much lower than the expected 2.5kg increase."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ethics & Privacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In conducting our research on attempt selection in powerlifting, it is paramount to address ethical and privacy concerns throughout the data science process. Our dataset, sourced from Openpowerlifting.com, provides valuable information on powerlifting competitions, but we recognize the importance of safeguarding the privacy of individuals involved. We will ensure that the data used is anonymized and that participants' consent to data usage is respected, given that the information is publicly available. Additionally, we acknowledge potential biases within the dataset, including under-representation of certain demographic groups in powerlifting competitions. To mitigate these biases, we will conduct thorough exploratory data analysis to identify any imbalances and employ strategies such as stratification or oversampling to ensure equitable analysis.\n",
    "\n",
    "Throughout the analysis, we will maintain transparency regarding the limitations and potential biases of the data. We will present findings in a fair and balanced manner, highlighting uncertainties and caveats associated with the analysis. Post-analysis, we will critically review our findings to assess their implications on different demographic groups and engage in ongoing dialogue with stakeholders to address concerns related to equity and fairness. Our research will adhere to ethical guidelines and best practices in data science, prioritizing transparency, accountability, and respect for the rights and dignity of research participants. By upholding these principles, we aim to contribute responsibly to the understanding of attempt weight selection in powerlifting while promoting inclusivity and ethical data use."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Discussion and Conclusion\n",
    "\n",
    "We set out to find what factors might effect the amount of weight a powerlifter adds to their attempts between their first and second attempt, and their second and third attempt in the various lifts of powerlifting.  \n",
    "\n",
    "To start, some early Exploratory Data Analysis(EDA) showed that we had female powerlifters dominating lighter weight classes, and men dominating higher weight classes. This could have affected our analysis, but some statistical tests comparing means of the highest weight class' jumps to the lowest weight class' jump showed only about a statistically significant 5% increase from the 43kg to 120+kg weight class. We later ran an Ordinary Least Squares regression to find how much an attempt percentage increase would change per kilogram of bodyweight, and found it to be -%0.0089 per kilogram of bodyweight, which put into the context of weight classes and other minimum weight jump rules, would not be helpful for determining an athlete's next attempt, offering no competitive advantage. Weight class had no significant effect on attempt selection.\n",
    "\n",
    "Then we looked between the Male and Female sex looking for differences in attempt selection, diving deeper into a per-event analysis. The dataset also contained an Mx class for those who don't identify as either, but there were only around 30 rows for them compared to the thousands for men and women and thus, were critically unrepresented, preventing us from drawing any conclusions on the Mx Sex. Analysis for this comprised of running an Ordinary Least Squares regression between percent of increases in weights between attempts and Sex, where Female athletes were shown to jump 0.6% more in weight in general, they also made the largest jumps on a per-event basis. Women jumped .5%, 1%, .5% more in weight than men on Squat, Bench, and Deadlift respectively, disproving our hypothesis that since Men lift more on the bench press, they would have the confidence to jump up more in weight. All results yielded a p-value of less than 0.001.\n",
    "\n",
    "Finally, we also wanted to see which event people might jump the most weight on, predicting that it would be the deadlift because it is the lift a majority of people lift the most weight on. This was proven to be incorrect with our Regression telling us that athletes lift 0.01% more during the deadlift than the other two events, which is not significantly more than the other two. This also yielded a p-value < 0.001.\n",
    "\n",
    "We found that attempt selection is pretty even across the board. Some factors like bodyweight actually have a negative effect on attempt selection, but not enough to make a competitive difference. Further research may compare other factors like whether more experienced lifters are more conservative on their attempt selection, whereas those in their first competition may opt for higher jumps due to their inexperience. It seems that neither Sex, Bodyweight, or Event made attempt selection exhibit different behaviours.\n",
    "\n",
    "\n",
    "# Team Contributions\n",
    "\n",
    "Speficy who did what.  This should be pretty granular, perhaps bullet points, no more than a few sentences per person.\n",
    "\n",
    "- Erick Amaro Hernandez (Research Question/Data/EDA/Analysis/Conclusion coding/writing)\n",
    "- Sanjith Devineni (EDA/Research Question/Proofreading/(Editing and correcting writing throughout project))\n",
    "- Alex Macias (Proofreading)\n",
    "- Lawrence Ong (Initial EDA/Analysis/(Editing writing throughout project))\n",
    "- Mingyang Yao (Data/(Editing writing throughout project))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}