CRICKET_1

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Dr. MGR Educational and Research university *

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123A

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Economics

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Nov 24, 2024

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docx

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import numpy as np import pandas as pd from Match_Extraction import df import time import statistics import matplotlib.pyplot as plt # gets the net run rate for a projected 50 overs score df["Adj RR Margin"] = abs(df["Team 1 Projected 50 Overs Score"] - df["Team 2 Projected 50 Overs Score"]) / 50 # gets rid of matches with no result df = df[df["Winner"] != 'No Result'] # finds the percentile for the 'BF Adj NRR' column df['RR Margin Percentile'] = df["Adj RR Margin"].rank(pct=True) # these are a list of teams to ignore teams_to_ignore = ['ICC World XI', 'Asia XI', 'Africa XI'] # this is used to calculate the elo ratings for each ODI side over time elo_dict = {'England': 1734, 'Australia': 1818, 'New Zealand': 1684, 'Pakistan': 1719, 'West Indies': 1748, 'India': 1765, 'East Africa': 1165, 'Sri Lanka': 1599, 'Canada': 1422, 'Zimbabwe': 1751, 'Bangladesh': 1633, 'South Africa': 1710, 'United Arab Emirates': 1323, 'Netherlands': 1490, 'Kenya': 1614, 'Scotland': 1466, 'Namibia': 1349, 'Hong Kong': 1302, 'United States of America': 1352, 'Bermuda': 1409, 'Ireland': 1641, 'Afghanistan': 1657, 'Papua New Guinea': 1226, 'Nepal': 1451, 'Oman': 1562, 'Jersey': 1415}

# in the form of {ground, city, country: [ground, city, country, 1st innings adj rr, 2nd innings adjusted rr, # total matches, bf_win, bf_lose]} ground_stats_dict = {} home_advantage_elo_boost = 220 start_time = time.time() for idx, match_facts in df.iterrows(): winner = match_facts["Winner"] bf = match_facts["Batting First"] bs = match_facts["Batting Second"] # doesn't change the ratings if there is no result if winner == 'No Result': continue # doesn't change the ratings if matches involve World or Continental XIs elif bf in teams_to_ignore: continue elif bs in teams_to_ignore: continue bf_pre_match_elo = elo_dict[bf] bs_pre_match_elo = elo_dict[bs] # assigns home advantage (if there is any) host_country = match_facts["Country"] ground_name = match_facts["Ground"] city = match_facts["City"] ground = ground_name + ", " + city + ", " + host_country if bf == host_country: bf_pre_match_elo += home_advantage_elo_boost elif bs == host_country: bs_pre_match_elo += home_advantage_elo_boost

# adds the elo boost to the team batting first match_type = match_facts["Series Type"] k_factor = 20 # changes the impact of the ratings depending on the kind of match # world cup matches have the most importance if match_type == "world-cup": k_factor *= 2 # Asia Cup and ICC Champions Trophy Matches also have more weight than regular ODI matches elif match_type == 'asia-cup' or match_type == 'bang': k_factor *= 1.5 # calculates the odds of the team batting first winning the match bf_win_expectancy = 1 / (10 ** ((bs_pre_match_elo - bf_pre_match_elo) / 400) + 1) # finds the adjusted run rate and finds the value to be used in elo points exchanges # 1.64 is the standard deviation of NRR for all ODI matches bf_adjusted_run_rate = match_facts["Team 1 Projected 50 Overs Score"] / 50 bs_adjusted_run_rate = match_facts["Team 2 Projected 50 Overs Score"] / 50 bf_nrr = bf_adjusted_run_rate - bs_adjusted_run_rate percentile = match_facts['RR Margin Percentile'] if percentile > 0.9999999999998945: percentile = 0.9999999999998945 elif percentile < 0.0000000000001055: percentile = 0.0000000000001055 z_score = statistics.NormalDist().inv_cdf(percentile) nrr_factor = 1.3 * z_score nrr_margin_increase = (0.75 + (nrr_factor - 3) / 8) # calculates the change in rating for each time if winner == bf: bf_change_in_rating = (1 - bf_win_expectancy) * k_factor * nrr_margin_increase elif winner == 'Tie':

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bf_change_in_rating = (0.5 - bf_win_expectancy) * k_factor else: bf_change_in_rating = (0 - bf_win_expectancy) * k_factor * nrr_margin_increase # updates the elo ratings after the match elo_dict[bf] += bf_change_in_rating elo_dict[bs] -= bf_change_in_rating if bf == host_country: home_advantage_elo_boost += 0.075 * bf_change_in_rating elif bs == host_country: home_advantage_elo_boost -= 0.075 * bf_change_in_rating # updates the ground information if ground not in ground_stats_dict: ground_stats_dict.update({ground: [ground_name, city, host_country, 0, 0, 0, 0, 0, 0]}) ground_stats_dict[ground][3] += 0.075 * bf_change_in_rating ground_total_matches = ground_stats_dict[ground][6] if ground_total_matches == 0: ground_stats_dict[ground][4] = bf_adjusted_run_rate ground_stats_dict[ground][5] = bs_adjusted_run_rate else: match_weight = (1 / ((ground_total_matches + 1) / 1.5)) ground_stats_dict[ground][4] = bf_adjusted_run_rate * match_weight + ground_stats_dict[ground] [4] * (1 - match_weight) ground_stats_dict[ground][5] = bs_adjusted_run_rate * match_weight + ground_stats_dict[ground] [5] * (1 - match_weight) ground_stats_dict[ground][6] += 1 if winner == bf: ground_stats_dict[ground][7] += 1 else: ground_stats_dict[ground][8] += 1

# creates a data frame on ground statistics ground_stats_df = pd.DataFrame(columns=["Ground Name", "City", "Country", "Batting First Elo Boost", "Adj 1st Innings Score", "Adj 2nd Innings Score", "Matches Completed", "Batting First Wins", "Batting Second Wins"], data=ground_stats_dict.values()) ground_stats_df["Adj 1st Innings Score"] = ground_stats_df["Adj 1st Innings Score"] * 50 ground_stats_df["Adj 2nd Innings Score"] = ground_stats_df["Adj 2nd Innings Score"] * 50 ground_stats_df.sort_values(by='Matches Completed', ascending=False, inplace=True) ground_stats_df["Batting First Win %"] = ground_stats_df["Batting First Wins"] / ground_stats_df["Matches Completed"] ground_stats_df["Batting Second Win %"] = ground_stats_df["Batting Second Wins"] / ground_stats_df["Matches Completed"] ground_stats_df.to_csv("ODI Grounds.csv", index=False, header=True) end_time = time.time() print("Elo Ratings Determined from Matches in", round((end_time - start_time) / 60, 2), "Minutes") # this is an elo dictionary that updates over time as new teams enter the ODI format time_sensitive_elo_dict = {} # in the form of {team: batting first elo adjustment} bat_first_elo_dict = {} # in the form of {ground: [adj run rate, total matches]} grounds_tilt_dict = {} # in the form of {team: [batting heavy score, total matches]} teams_tilt_dict = {} # these are used to update the tilt and rating over time for a line graph elo_line_graph_dict = {"Date": []} bat_first_elo_line_graph = {"Date": []} teams_tilt_line_graph = {"Date": []}

teams_of_interest = ['West Indies', 'India', 'Australia', 'Pakistan', 'Sri Lanka', 'New Zealand', 'South Africa', 'England'] for team in teams_of_interest: elo_line_graph_dict.update({team: []}) bat_first_elo_line_graph.update({team: []}) teams_tilt_line_graph.update({team: []}) home_advantage_elo_boost = 220 start_time = time.time() for idx, match_facts in df.iterrows(): winner = match_facts["Winner"] bf = match_facts["Batting First"] bs = match_facts["Batting Second"] # doesn't change the ratings if there is no result if winner == 'No Result': continue # doesn't change the ratings if matches involve World or Continental XIs elif bf in teams_to_ignore: continue elif bs in teams_to_ignore: continue date = match_facts['Date'] bf_pre_match_elo = elo_dict[bf] bs_pre_match_elo = elo_dict[bs] # assigns home advantage (if there is any) host_country = match_facts["Country"] ground_name = match_facts["Ground"] city = match_facts["City"] ground = ground_name + "," + city + "," + host_country if bf == host_country:

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bf_pre_match_elo += home_advantage_elo_boost elif bs == host_country: bs_pre_match_elo += home_advantage_elo_boost # adds the elo boost to the team batting first based on the ground and the team bf_elo_boost = ground_stats_df[(ground_stats_df["Country"] == host_country) & (ground_stats_df["City"] == city) & (ground_stats_df["Ground Name"] == ground_name)].iloc[0]["Batting First Elo Boost"] if bf not in bat_first_elo_dict: bat_first_elo_dict.update({bf: 0}) if bs not in bat_first_elo_dict: bat_first_elo_dict.update({bs: 0}) bf_elo_boost += bat_first_elo_dict[bf] + bat_first_elo_dict[bs] bf_pre_match_elo += bf_elo_boost match_type = match_facts["Series Type"] k_factor = 20 # changes the impact of the ratings depending on the kind of match # world cup matches have the most importance if match_type == "world-cup": k_factor *= 2 # Asia Cup and ICC Champions Trophy Matches also have more weight than regular ODI matches elif match_type == 'asia-cup' or match_type == 'bang': k_factor *= 1.5 # calculates the odds of the team batting first winning the match bf_win_expectancy = 1 / (10 ** ((bs_pre_match_elo - bf_pre_match_elo) / 400) + 1) # finds the adjusted run rate and finds the value to be used in elo points exchanges # 1.64 is the standard deviation of NRR for all ODI matches bf_adjusted_run_rate = match_facts["Team 1 Projected 50 Overs Score"] / 50 bs_adjusted_run_rate = match_facts["Team 2 Projected 50 Overs Score"] / 50

bf_nrr = bf_adjusted_run_rate - bs_adjusted_run_rate percentile = match_facts['RR Margin Percentile'] if percentile > 0.9999999999998945: percentile = 0.9999999999998945 elif percentile < 0.0000000000001055: percentile = 0.0000000000001055 z_score = statistics.NormalDist().inv_cdf(percentile) nrr_factor = 1.3 * z_score nrr_margin_increase = (0.75 + (nrr_factor - 3) / 8) # calculates the change in rating for each time if winner == bf: bf_change_in_rating = (1 - bf_win_expectancy) * k_factor * nrr_margin_increase elif winner == 'Tie': bf_change_in_rating = (0.5 - bf_win_expectancy) * k_factor else: bf_change_in_rating = (0 - bf_win_expectancy) * k_factor * nrr_margin_increase # updates the elo ratings after the match elo_dict[bf] += bf_change_in_rating elo_dict[bs] -= bf_change_in_rating time_sensitive_elo_dict.update({bf: elo_dict[bf]}) time_sensitive_elo_dict.update({bs: elo_dict[bs]}) # updates for home field advantage updates if bf == host_country: home_advantage_elo_boost += 0.075 * bf_change_in_rating elif bs == host_country: home_advantage_elo_boost -= 0.075 * bf_change_in_rating # updates for bat first elo ratings bat_first_elo_dict[bf] += 0.075 * bf_change_in_rating bat_first_elo_dict[bs] += 0.075 * bf_change_in_rating

# updates the ground tilt (high run vs low run pitch) avg_rr = (bf_adjusted_run_rate + bs_adjusted_run_rate) / 2 if ground not in grounds_tilt_dict: grounds_tilt_dict.update({ground: [avg_rr, 1]}) else: match_weight = 1 / ((grounds_tilt_dict[ground][1] + 1) / 1.25) grounds_tilt_dict[ground][0] = match_weight * avg_rr + (1 - match_weight) * grounds_tilt_dict[ground][0] # updates the tilt (bowling or batting strength) of both teams ground_adj_rr = grounds_tilt_dict[ground][0] match_runs_percentile = statistics.NormalDist(mu=ground_adj_rr, sigma=0.9386).cdf(avg_rr) if bf not in teams_tilt_dict: teams_tilt_dict.update({bf: [match_runs_percentile, 1]}) else: match_weight = 1 / ((teams_tilt_dict[bf][1] + 1) / 1.25) teams_tilt_dict[bf][0] = match_weight * match_runs_percentile + teams_tilt_dict[bf][0] * (1 - match_weight) if bs not in teams_tilt_dict: teams_tilt_dict.update({bs: [match_runs_percentile, 1]}) else: match_weight = 1 / ((teams_tilt_dict[bf][1] + 1) / 1.25) teams_tilt_dict[bs][0] = match_weight * match_runs_percentile + teams_tilt_dict[bs][0] * (1 - match_weight) # records the ratings to use in a line graph elo_line_graph_dict["Date"].append(date) bat_first_elo_line_graph["Date"].append(date) teams_tilt_line_graph["Date"].append(date) for team in teams_of_interest: if team not in time_sensitive_elo_dict: elo_line_graph_dict[team].append(np.NaN)

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bat_first_elo_line_graph[team].append(np.NaN) teams_tilt_line_graph[team].append(np.NaN) else: elo_line_graph_dict[team].append(elo_dict[team]) bat_first_elo_line_graph[team].append(bat_first_elo_dict[team]) teams_tilt_line_graph[team].append(teams_tilt_dict[team][0]) if idx % 100 == 0: print(match_facts['Date']) print() time_sensitive_elo_dict = dict(sorted(time_sensitive_elo_dict.items(), key=lambda item: item[1], reverse=True)) elo_ratings_df = pd.DataFrame(list(time_sensitive_elo_dict.items()), columns=["Team", "Rating"]) rank = 0 ranked_bf_elo_dict = {} ranked_batting_tilt_dict = {} for team, rating in time_sensitive_elo_dict.items(): rank += 1 ranked_bf_elo_dict.update({team: bat_first_elo_dict[team]}) ranked_batting_tilt_dict.update({team: teams_tilt_dict[team][0]}) print(rank, team, rating, bat_first_elo_dict[team], teams_tilt_dict[team][0]) print() print("Current Ratings:") print(match_facts['Date']) print() time_sensitive_elo_dict = dict(sorted(time_sensitive_elo_dict.items(), key=lambda item: item[1], reverse=True)) elo_ratings_df = pd.DataFrame(list(time_sensitive_elo_dict.items()), columns=["Team", "Rating"]) rank = 0 ranked_bf_elo_dict = {}

ranked_batting_tilt_dict = {} for team, rating in time_sensitive_elo_dict.items(): rank += 1 ranked_bf_elo_dict.update({team: bat_first_elo_dict[team]}) ranked_batting_tilt_dict.update({team: teams_tilt_dict[team][0]}) print(rank, team, rating, bat_first_elo_dict[team], teams_tilt_dict[team][0]) print() elo_ratings_df['Bat First Elo Adj'] = list(ranked_bf_elo_dict.values()) # elo_ratings_df['Batting Dependency Score'] = list(ranked_batting_tilt_dict.values()) elo_ratings_df['Rank'] = list(range(1, 27)) elo_ratings_df.set_index("Rank", inplace=True) elo_ratings_df.to_csv("ODI_Elo_Ratings.csv", index=True, header=True) end_time = time.time() print("Ground-Adjusted Elo Ratings Determined from Matches in", round((end_time - start_time) / 60, 2), "Minutes") elo_line_graph_df = pd.DataFrame(elo_line_graph_dict) bat_first_line_graph_df = pd.DataFrame(bat_first_elo_line_graph) team_tilt_line_graph_df = pd.DataFrame(teams_tilt_line_graph)

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