├── SavedModels └── finalized_model.pkl ├── requirements.txt ├── availableStats.py ├── configureCWD.py ├── customHeaders.py ├── teamIds.py ├── getStats.py ├── README.md ├── getDailyMatchups.py ├── standardizeStats.py ├── makePastPredictions.py ├── nbaPredict.py ├── createModel.py └── Data └── gamesWithInfo2018-19.csv /SavedModels/finalized_model.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JakeKandell/NBA-Predict/HEAD/SavedModels/finalized_model.pkl -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | certifi==2019.11.28 2 | chardet==3.0.4 3 | idna==2.8 4 | joblib==0.14.1 5 | nba-api==1.1.8 6 | numpy==1.17.4 7 | pandas==0.25.3 8 | pkg-resources==0.0.0 9 | python-dateutil==2.8.1 10 | pytz==2019.3 11 | requests==2.22.0 12 | scikit-learn==0.21.2 13 | scipy==1.4.1 14 | six==1.13.0 15 | urllib3==1.26.5 16 | -------------------------------------------------------------------------------- /availableStats.py: -------------------------------------------------------------------------------- 1 | # Dictionary holding stats that are analyzed for each game and their type 2 | # Update if new stats are added 3 | 4 | availableStats = {'W_PCT':'Base', 5 | 'REB':'Base', 6 | 'TOV':'Base', 7 | 'PLUS_MINUS':'Base', 8 | 'OFF_RATING':'Advanced', 9 | 'DEF_RATING':'Advanced', 10 | 'TS_PCT':'Advanced'} -------------------------------------------------------------------------------- /configureCWD.py: -------------------------------------------------------------------------------- 1 | # configureCWD.py - Sets current working directory relative to where program folder is located 2 | 3 | import os 4 | 5 | # Sets current working directory relative to where program folder is located 6 | def setCurrentWorkingDirectory(directoryName): 7 | 8 | programDirectory = os.path.dirname(os.path.abspath(__file__)) 9 | newCurrentWorkingDirectory = os.path.join(programDirectory, directoryName) 10 | os.chdir(newCurrentWorkingDirectory) -------------------------------------------------------------------------------- /customHeaders.py: -------------------------------------------------------------------------------- 1 | # customHeaders.py 2 | # Used to make requests seem like they're coming from browser 3 | customHeaders = { 4 | 'Host': 'stats.nba.com', 5 | 'Connection': 'keep-alive', 6 | 'Cache-Control': 'max-age=0', 7 | 'Upgrade-Insecure-Requests': '1', 8 | 'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.100 Safari/537.36', 9 | 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3', 10 | 'Accept-Encoding': 'gzip, deflate, br', 11 | 'Accept-Language': 'en-US,en;q=0.9', 12 | 'Referer': 'https://stats.nba.com/', 13 | 'x-nba-stats-origin': 'stats', 14 | 'x-nba-stats-token': 'true', 15 | } -------------------------------------------------------------------------------- /teamIds.py: -------------------------------------------------------------------------------- 1 | # teamIds.py - Dictionary holding stats.nba.com IDs for each NBA team 2 | 3 | teams = { 4 | "Atlanta Hawks": 1610612737, 5 | "Boston Celtics":1610612738, 6 | "Brooklyn Nets": 1610612751, 7 | "Charlotte Hornets": 1610612766, 8 | "Charlotte Bobcats": 1610612766, 9 | "Chicago Bulls": 1610612741, 10 | "Cleveland Cavaliers": 1610612739, 11 | "Dallas Mavericks": 1610612742, 12 | "Denver Nuggets": 1610612743, 13 | "Detroit Pistons": 1610612765, 14 | "Golden State Warriors": 1610612744, 15 | "Houston Rockets": 1610612745, 16 | "Indiana Pacers": 1610612754, 17 | "LA Clippers": 1610612746, 18 | "Los Angeles Clippers": 1610612746, 19 | "Los Angeles Lakers": 1610612747, 20 | "Memphis Grizzlies": 1610612763, 21 | "Miami Heat": 1610612748, 22 | "Milwaukee Bucks": 1610612749, 23 | "Minnesota Timberwolves": 1610612750, 24 | "New Jersey Nets": 1610612751, 25 | "New Orleans Hornets": 1610612740, 26 | "New Orleans Pelicans": 1610612740, 27 | "New York Knicks": 1610612752, 28 | "Oklahoma City Thunder": 1610612760, 29 | "Orlando Magic": 1610612753, 30 | "Philadelphia 76ers": 1610612755, 31 | "Phoenix Suns": 1610612756, 32 | "Portland Trail Blazers": 1610612757, 33 | "Sacramento Kings": 1610612758, 34 | "San Antonio Spurs": 1610612759, 35 | "Toronto Raptors": 1610612761, 36 | "Utah Jazz": 1610612762, 37 | "Washington Wizards": 1610612764, 38 | } 39 | -------------------------------------------------------------------------------- /getStats.py: -------------------------------------------------------------------------------- 1 | # getStats.py - Obtains a grouping of stats for any team in the NBA 2 | 3 | from teamIds import teams 4 | from nba_api.stats.endpoints import teamdashboardbygeneralsplits, leaguedashteamstats 5 | import time 6 | from customHeaders import customHeaders 7 | 8 | # Returns various stats for inputted team in a dictionary 9 | # team should match team name in teamIds.py 10 | # startDate and endDate should be in format 'mm/dd/yyyy' 11 | def getStatsForTeam(team, startDate, endDate, season='2019-20'): 12 | 13 | time.sleep(1) 14 | # Uses NBA_API to access the dictionary holding basic stats for every team per 100 possessions 15 | generalTeamInfo = teamdashboardbygeneralsplits.TeamDashboardByGeneralSplits(team_id=teams[team], per_mode_detailed='Per100Possessions', date_from_nullable=startDate, date_to_nullable=endDate, season=season, headers=customHeaders, timeout=120) 16 | generalTeamDict = generalTeamInfo.get_normalized_dict() 17 | generalTeamDashboard = generalTeamDict['OverallTeamDashboard'][0] 18 | 19 | # Returns Win PCT, Rebounds, Turnovers, and Plus Minus 20 | winPercentage = generalTeamDashboard['W_PCT'] 21 | rebounds = generalTeamDashboard['REB'] 22 | turnovers = generalTeamDashboard['TOV'] 23 | plusMinus = generalTeamDashboard['PLUS_MINUS'] 24 | 25 | # Uses NBA_API to access the dictionary holding advanced stats for every team 26 | advancedTeamInfo = teamdashboardbygeneralsplits.TeamDashboardByGeneralSplits(team_id=teams[team], measure_type_detailed_defense='Advanced', date_from_nullable=startDate, date_to_nullable=endDate, season=season, headers=customHeaders, timeout=120) 27 | advancedTeamDict = advancedTeamInfo.get_normalized_dict() 28 | advancedTeamDashboard = advancedTeamDict['OverallTeamDashboard'][0] 29 | 30 | # Variables holding OFF Rating, DEF Rating, and TS% 31 | offensiveRating = advancedTeamDashboard['OFF_RATING'] 32 | defensiveRating = advancedTeamDashboard['DEF_RATING'] 33 | trueShootingPercentage = advancedTeamDashboard['TS_PCT'] 34 | 35 | # Puts all the stats for specified team into a dictionary 36 | allStats = { 37 | 'W_PCT':winPercentage, 38 | 'REB':rebounds, 39 | 'TOV':turnovers, 40 | 'PLUS_MINUS':plusMinus, 41 | 'OFF_RATING':offensiveRating, 42 | 'DEF_RATING': defensiveRating, 43 | 'TS_PCT':trueShootingPercentage, 44 | } 45 | 46 | return allStats 47 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # NBA-Predict 2 | 3 | 4 | Predicts NBA Games Using a Logistic Regression Model in Python 5 | 6 | # Model 7 | The model uses eight factors scraped from stats.nba.com to determine the predicted result of an NBA game. Each stat is adjusted to per 100 possessions to ensure pace has no impact on the predictions. 8 | 9 | * Home Team 10 | * Win Percentage 11 | * Rebounds 12 | * Turnovers 13 | * Plus Minus 14 | * Offensive Rating 15 | * Defensive Rating 16 | * True Shooting Percentage 17 | 18 | # Usage 19 | 20 | ### **Installation** 21 | 22 | ```bash 23 | pip3 install -r requirements.txt 24 | ``` 25 | 26 | ### **Daily Predictions** 27 | 1. Open nbaPredict.py 28 | 2. Edit the call to makeInterpretPrediction with desired date of games, season, and the start date of the season 29 | ![NBA-Predict Image](https://user-images.githubusercontent.com/24983943/71434218-7e677e00-26b1-11ea-8a20-cad8d410b251.png) 30 | 3. Run the program either through the terminal or an IDE 31 | 4. Wait ~1-3 minutes for model to finish scraping stats and predicting outcomes 32 | 5. Outcomes are outputted as the percent chance that the home team will defeat the away team 33 | ![Screen Shot 2019-08-01 at 5 41 22 PM](https://user-images.githubusercontent.com/24983943/62329326-9977ee00-b483-11e9-9ce3-b9c9cdf78938.png) 34 | 35 | ### **Past Predictions** 36 | 1. Open makePastPredictions.py 37 | 2. Edit the call to makePastPredictions with desired start date, end date, season, start date of the season, and output 38 | filenames. NOTE: The start date should be at least three days after the season begins and the end date is non-inclusive. 39 | 40 | ![Screen Shot 2019-10-11 at 1 41 42 AM](https://user-images.githubusercontent.com/24983943/66627069-7f1b6880-ebc8-11e9-9a94-981b2fa61f1e.png) 41 | 3. Run the program either through the terminal or an IDE 42 | 4. Two CSV files will be saved in the Data folder. One holds the gameData and the other holds the predictions for the games. The time to execute will vary greatly from a couple minutes to a several hours depending on the range between the start and end date. 43 | 44 | 45 | # Accuracy Information 46 | ### **Confusion Matrix** 47 | 48 | ![rsz_1confusionmatrix](https://user-images.githubusercontent.com/24983943/71774907-20565880-2f45-11ea-85c2-a4073a0ac7df.png) 49 | 50 | **Accuracy: 0.7037 (532/756)** 51 | 52 | **Precision: 0.7367 (375/509)** 53 | 54 | **Recall: 0.8065 (375/465)** 55 | -------------------------------------------------------------------------------- /getDailyMatchups.py: -------------------------------------------------------------------------------- 1 | # getDailyMatchups.py - Finds the daily NBA games 2 | 3 | from nba_api.stats.endpoints import leaguegamelog, scoreboard 4 | from teamIds import teams 5 | from customHeaders import customHeaders 6 | 7 | # Function to get you the games on a specified date (Home vs. Away) 8 | # Used for dates in the past 9 | # Return value is a list where index 0 is a dict holding the games and index 1 is the result of the games 10 | # Enter a date in the format mm/dd/yyyy and season in the format yyyy-yy 11 | def dailyMatchupsPast(date, season): 12 | 13 | # Obtains a list of teams who played on specified date 14 | dailyMatchups = leaguegamelog.LeagueGameLog(season=season, league_id='00', season_type_all_star='Regular Season', date_from_nullable=date,date_to_nullable=date, headers=customHeaders,timeout=60) 15 | dailyMatchupsDict = dailyMatchups.get_normalized_dict() 16 | listOfTeams = dailyMatchupsDict['LeagueGameLog'] 17 | 18 | winLossList = [] 19 | homeAwayDict = {} 20 | for i in range(0,len(listOfTeams),2): # Loops through every other team 21 | if '@' in listOfTeams[i]['MATCHUP']: # @ in matchup indicates that the current team is away 22 | awayTeam = listOfTeams[i]['TEAM_NAME'] 23 | homeTeam = listOfTeams[i+1]['TEAM_NAME'] 24 | 25 | winLossList.append(listOfTeams[i+1]['WL']) # Appends if the home team won or lost to list 26 | 27 | else: 28 | awayTeam = listOfTeams[i+1]['TEAM_NAME'] 29 | homeTeam = listOfTeams[i]['TEAM_NAME'] 30 | 31 | winLossList.append(listOfTeams[i]['WL']) # Appends if the home team won or lost to the list 32 | 33 | homeAwayDict.update({homeTeam:awayTeam}) # Adds current game to list of all games for that day 34 | 35 | matchupsResultCombined = [homeAwayDict, winLossList] # Combines games and win/loss results into one list 36 | return(matchupsResultCombined) 37 | 38 | 39 | # Function to get you the games on a specified date (Home vs. Away) 40 | # Used for dates in the present or future 41 | # Return value is a list where index 0 is a dict holding the games {Home:Away} 42 | # Enter a date in the format mm/dd/yyyy 43 | def dailyMatchupsPresent(date): 44 | 45 | # Obtains all games that are set to occur on specified date 46 | dailyMatchups = scoreboard.Scoreboard(league_id='00', game_date=date, headers=customHeaders, timeout=120) 47 | dailyMatchupsDict = dailyMatchups.get_normalized_dict() 48 | listOfGames = dailyMatchupsDict['GameHeader'] 49 | 50 | homeAwayDict = {} 51 | 52 | for game in listOfGames: # Loops through each game on date 53 | 54 | homeTeamID = game['HOME_TEAM_ID'] 55 | 56 | for team, teamID in teams.items(): # Finds name of the home team that corresponds with teamID 57 | if teamID == homeTeamID: 58 | homeTeamName = team 59 | 60 | awayTeamID = game['VISITOR_TEAM_ID'] 61 | 62 | for team, teamID in teams.items(): # Finds name of the away team that corresponds with teamID 63 | if teamID == awayTeamID: 64 | awayTeamName = team 65 | 66 | homeAwayDict.update({homeTeamName:awayTeamName}) 67 | 68 | return homeAwayDict 69 | -------------------------------------------------------------------------------- /standardizeStats.py: -------------------------------------------------------------------------------- 1 | # standardizeStats.py - Uses Z Scores ((Obs - Mean) / St Dev.) to standardize any of the different statistics scraped 2 | 3 | from nba_api.stats.endpoints import leaguedashteamstats 4 | import statistics 5 | from getStats import getStatsForTeam 6 | import time 7 | from customHeaders import customHeaders 8 | 9 | # Finds league mean for the entered basic or advanced statistic (statType = 'Base' or 'Advanced') 10 | def basicOrAdvancedStatMean(startDate, endDate, stat,statType = 'Base', season='2018-19'): 11 | 12 | time.sleep(.2) 13 | # Gets list of dictionaries with stats for every team 14 | allTeamsInfo = leaguedashteamstats.LeagueDashTeamStats(per_mode_detailed='Per100Possessions', 15 | measure_type_detailed_defense=statType, 16 | date_from_nullable=startDate, 17 | date_to_nullable=endDate, 18 | season=season, 19 | headers=customHeaders, 20 | timeout=120) 21 | allTeamsDict = allTeamsInfo.get_normalized_dict() 22 | allTeamsList = allTeamsDict['LeagueDashTeamStats'] 23 | 24 | specificStatAllTeams = [] 25 | for i in range(len(allTeamsList)): # Loops through and appends specific stat to new list until every team's stat has been added 26 | specificStatAllTeams.append(allTeamsList[i][stat]) 27 | 28 | mean = statistics.mean(specificStatAllTeams) # Finds mean of stat 29 | return mean 30 | 31 | 32 | # Finds league standard deviation for the entered basic or advanced statistic (statType = 'Base' or 'Advanced') 33 | def basicOrAdvancedStatStandardDeviation(startDate, endDate, stat,statType = 'Base', season='2018-19'): 34 | 35 | time.sleep(.2) 36 | # Gets list of dictionaries with stats for every team 37 | allTeamsInfo = leaguedashteamstats.LeagueDashTeamStats(per_mode_detailed='Per100Possessions', 38 | measure_type_detailed_defense=statType, 39 | date_from_nullable=startDate, 40 | date_to_nullable=endDate, 41 | season=season, 42 | headers=customHeaders, 43 | timeout=120 44 | ) 45 | allTeamsDict = allTeamsInfo.get_normalized_dict() 46 | allTeamsList = allTeamsDict['LeagueDashTeamStats'] 47 | 48 | specificStatAllTeams = [] 49 | for i in range(len(allTeamsList)): # Loops and appends specific stat to new list until every team's stat has been added 50 | specificStatAllTeams.append(allTeamsList[i][stat]) 51 | 52 | standardDeviation = statistics.stdev(specificStatAllTeams) # Finds standard deviation of stat 53 | return standardDeviation 54 | 55 | 56 | # Returns a standardized version of each data point via the z-score method 57 | def basicOrAdvancedStatZScore(observedStat, mean, standardDeviation): 58 | 59 | zScore = (observedStat-mean)/standardDeviation # Calculation for z-score 60 | 61 | return(zScore) 62 | -------------------------------------------------------------------------------- /makePastPredictions.py: -------------------------------------------------------------------------------- 1 | # makePastPredictions.py - Used to predicts specified range of past NBA Games 2 | 3 | import pickle 4 | import pandas as pd 5 | 6 | from createModel import getTrainingSet, createDataFrame 7 | from configureCWD import setCurrentWorkingDirectory 8 | 9 | 10 | # Exports game information for all games between specified time period to 'Data' Folder within project 11 | # End date will not be included in range 12 | # season must be in form 'yyyy-yy' and startDateOfSeason must be in form 'mm/dd/yyyy' 13 | # filename must end in '.csv' 14 | def getTrainingSetCSV(startYear, startMonth, startDay, endYear, endMonth, endDay, season, startDateOfSeason, filename='gamesWithInfo.csv'): 15 | 16 | # Gets date, teams, and z-score difs for every game within range 17 | rangeOfGames = getTrainingSet(startYear, startMonth, startDay, endYear, endMonth, endDay, season, startDateOfSeason) 18 | rangeOfGamesDataframe = createDataFrame(rangeOfGames) 19 | 20 | setCurrentWorkingDirectory('Data') 21 | 22 | rangeOfGamesDataframe.to_csv(filename) 23 | 24 | 25 | # Creates a csv file that gives predictions for range of games 26 | # Prints accuracy of model in predicting games for specified range 27 | # gameDataFilename and outputFilename must be '.csv' files 28 | def getPredictionsCSV(gameDataFilename, outputFilename): 29 | 30 | setCurrentWorkingDirectory('Data') 31 | 32 | gamesWithZScoreDifs = pd.read_csv(gameDataFilename) 33 | 34 | withoutNums = gamesWithZScoreDifs.loc[:, 'Home':'Date'] # Slices dataframe to only includes home through date 35 | justZScoreDifs = gamesWithZScoreDifs.loc[:, 'W_PCT':'TS_PCT'] # Slices dataframe to only include statistical differences 36 | 37 | setCurrentWorkingDirectory('SavedModels') 38 | with open('finalized_model.pkl', 'rb') as file: # Change filename here if model is named differently 39 | pickleModel = pickle.load(file) 40 | 41 | predictions = pickleModel.predict(justZScoreDifs) # Creates list of predicted winners and losers 42 | probPredictions = pickleModel.predict_proba(justZScoreDifs) # Creates list of probabilities that home team wins 43 | 44 | numCorrect = 0 45 | numWrong = 0 46 | allGames = [] 47 | 48 | for i in range(len(probPredictions)): 49 | 50 | winProbability = probPredictions[i][1] 51 | homeTeam = withoutNums.iloc[i, 0] 52 | awayTeam = withoutNums.iloc[i, 1] 53 | date = withoutNums.iloc[i, 10] 54 | 55 | currentGameWithPred = [date, homeTeam, awayTeam, winProbability] 56 | 57 | allGames.append(currentGameWithPred) 58 | 59 | # Creates dataframe that holds all games info and predictions 60 | predictionsDF = pd.DataFrame( 61 | allGames, 62 | columns=['Date', 'Home', 'Away', 'Home Team Win Probability'] 63 | ) 64 | 65 | setCurrentWorkingDirectory('Data') 66 | predictionsDF.to_csv(outputFilename) # Saves game info with predictions in data folder as csv file 67 | 68 | value = withoutNums.iloc[i,9] 69 | if value == predictions[i]: 70 | numCorrect += 1 71 | else : 72 | numWrong += 1 73 | 74 | print('Accuracy:') 75 | print((numCorrect)/(numCorrect+numWrong)) # Prints accuracy of model in predicting games for specified range 76 | 77 | 78 | # Generates probability predictions over specified range of games exports them to a csv with game info 79 | # gameDataFilename and outputFilename must end in '.csv' 80 | # season must be in form 'yyyy-yy' and startDateOfSeason must be in form 'mm/dd/yyyy' 81 | def makePastPredictions(startYear, startMonth, startDay, endYear, endMonth, endDay, season, startDateOfSeason, 82 | gameDataFilename='gamesWithInfo.csv', outputFilename='predictions.csv'): 83 | 84 | # Obtains info for range of games 85 | getTrainingSetCSV(startYear, startMonth, startDay, endYear, endMonth, endDay, season, startDateOfSeason, 86 | gameDataFilename) 87 | # Makes probabilities for range of games 88 | getPredictionsCSV(gameDataFilename, outputFilename) 89 | 90 | 91 | # start date (yyyy, m, d) (must be at least three days after start of season), end date (yyyy, m, d) (non-inclusive), 92 | # season(yyyy-yy), start date of season (mm/dd/yyyy), .csv filename for games with z score differences, 93 | # .csv filename for games with predictions 94 | # EDIT THIS 95 | makePastPredictions(2018, 12, 28, 2019, 1, 13, '2018-19', '10/16/2018', 96 | 'gamesWithInfo.csv', 'predictions.csv') 97 | 98 | -------------------------------------------------------------------------------- /nbaPredict.py: -------------------------------------------------------------------------------- 1 | # nbaPredict.py - Predicts results of NBA games on a specified date 2 | # Call makeInterpretPrediction with current date, season, and start date of season to run predictions 3 | 4 | import pickle 5 | import pandas as pd 6 | 7 | from getDailyMatchups import dailyMatchupsPresent 8 | from createModel import createMeanStandardDeviationDicts, zScoreDifferential 9 | from availableStats import availableStats 10 | from getStats import getStatsForTeam 11 | from configureCWD import setCurrentWorkingDirectory 12 | 13 | 14 | # Returns list of games with Z-Score differentials between teams to be put into a Pandas dataframe 15 | # startDate & endDate should be 'mm/dd/yyyy' form 16 | def dailyGamesDataFrame(dailyGames, meanDict, standardDeviationDict, startDate, endDate, season): 17 | 18 | fullDataFrame = [] 19 | 20 | for homeTeam,awayTeam in dailyGames.items(): 21 | 22 | homeTeamStats = getStatsForTeam(homeTeam, startDate, endDate, season) 23 | awayTeamStats = getStatsForTeam(awayTeam, startDate, endDate, season) 24 | 25 | currentGame = [homeTeam,awayTeam] 26 | 27 | for stat,statType in availableStats.items(): # Finds Z Score Dif for stats listed above and adds them to list 28 | zScoreDif = zScoreDifferential(homeTeamStats[stat], awayTeamStats[stat], meanDict[stat], standardDeviationDict[stat]) 29 | currentGame.append(zScoreDif) 30 | 31 | fullDataFrame.append(currentGame) # Adds this list to list of all games on specified date 32 | 33 | return(fullDataFrame) 34 | 35 | 36 | # Returns a list 37 | # Index 0 is the dailyGames in dict form {Home:Away} 38 | # Index 1 is a list with the prediction probabilities for each game [[lossProb, winProb]] 39 | # currentDate should be in form 'mm/dd/yyyy' and season in form 'yyyy-yy' 40 | def predictDailyGames(currentDate, season, startOfSeason): 41 | 42 | dailyGames = dailyMatchupsPresent(currentDate) # Gets all games for specified date 43 | meanDict, standardDeviationDict = createMeanStandardDeviationDicts(startOfSeason, currentDate, season) 44 | dailyGamesList = dailyGamesDataFrame(dailyGames, meanDict, standardDeviationDict, startOfSeason, currentDate, season) 45 | 46 | # Pandas dataframe holding daily games and Z-Score differentials between teams 47 | gamesWithZScoreDifs = pd.DataFrame( 48 | dailyGamesList, 49 | columns=['Home', 'Away', 'W_PCT', 'REB', 'TOV', 'PLUS_MINUS', 'OFF_RATING', 'DEF_RATING', 'TS_PCT'] 50 | ) 51 | 52 | justZScoreDifs = gamesWithZScoreDifs.loc[:,'W_PCT':'TS_PCT'] # Slices only the features used in the model 53 | 54 | with open('finalized_model.pkl', 'rb') as file: # Change filename here if model is named differently 55 | pickleModel = pickle.load(file) 56 | 57 | predictions = pickleModel.predict_proba(justZScoreDifs) # Predicts the probability that the home team loses/wins 58 | 59 | gamesWithPredictions = [dailyGames, predictions] 60 | return gamesWithPredictions 61 | 62 | 63 | # Returns the percent chance that the home team will defeat the away team for each game 64 | # gamesWithPredictions should be in form [dailyGames, predictionsList] 65 | def interpretPredictions(gamesWithPredictions): 66 | 67 | dailyGames = gamesWithPredictions[0] # Dict holding daily matchups 68 | probabilityPredictions = gamesWithPredictions[1] # List of lists holding probs of loss/win for home team 69 | 70 | for gameNum in range(len(probabilityPredictions)): # Loops through each game 71 | winProb = probabilityPredictions[gameNum][1] 72 | winProbRounded = round(winProb,4) 73 | winProbPercent = "{:.2%}".format(winProbRounded) # Formulates percent chance that home team wins 74 | 75 | homeTeam = list(dailyGames.keys())[gameNum] 76 | awayTeam = list(dailyGames.values())[gameNum] 77 | 78 | print('There is a ' + winProbPercent + ' chance that the ' + homeTeam + ' will defeat the ' + awayTeam + '.') 79 | 80 | 81 | # Fetches games on set date and returns predictions for each game 82 | # currentDate/startOfSeason should be in form 'mm/dd/yyyy' and season in form 'yyyy-yy' 83 | # Start of 2019-20 season was 10/2/2019 84 | def makeInterpretPredictions(currentDate, season, startOfSeason): 85 | 86 | setCurrentWorkingDirectory('SavedModels') 87 | 88 | print('Predictions for ' + currentDate + ':') 89 | predictions = predictDailyGames(currentDate, season, startOfSeason) 90 | interpretPredictions(predictions) 91 | 92 | 93 | # EDIT THIS 94 | # First arg is date to predict (mm/dd/yyyy), second is season (yyyy-yy), and third is start date of season (mm/dd/yyyy) 95 | makeInterpretPredictions('01/04/2020', '2019-20', '10/22/2019') 96 | -------------------------------------------------------------------------------- /createModel.py: -------------------------------------------------------------------------------- 1 | # createModel.py - Used to train, test, and create the model 2 | # Call createModel() to generate a new model 3 | # May need to edit which lines are commented out based on what range of game data you would like to use 4 | 5 | from standardizeStats import basicOrAdvancedStatZScore, basicOrAdvancedStatStandardDeviation, basicOrAdvancedStatMean 6 | from getDailyMatchups import dailyMatchupsPast 7 | from getStats import getStatsForTeam 8 | from availableStats import availableStats 9 | from configureCWD import setCurrentWorkingDirectory 10 | 11 | from sklearn.model_selection import train_test_split 12 | from sklearn.linear_model import LogisticRegression 13 | from sklearn import metrics 14 | import pandas as pd 15 | import pickle 16 | 17 | from datetime import timedelta, date 18 | 19 | 20 | # Calculates the zScore differential between two teams for a specified stat 21 | def zScoreDifferential(observedStatHome, observedStatAway, mean, standardDeviation): 22 | 23 | homeTeamZScore = basicOrAdvancedStatZScore(observedStatHome, mean, standardDeviation) 24 | awayTeamZScore = basicOrAdvancedStatZScore(observedStatAway, mean, standardDeviation) 25 | 26 | differenceInZScore = homeTeamZScore - awayTeamZScore 27 | return differenceInZScore 28 | 29 | 30 | # Used to combine and format all the data to be put into a pandas dataframe 31 | # dailyGames should be list where index 0 is a dictionary holding the games and index 1 is a list holding the results 32 | def infoToDataFrame(dailyGames, meanDict, standardDeviationDict, startDate, endDate, season): 33 | 34 | fullDataFrame = [] 35 | gameNumber = 0 # Counter to match the result of the game with the correct game 36 | dailyResults = dailyGames[1] # List of results for the games 37 | 38 | for homeTeam,awayTeam in dailyGames[0].items(): 39 | 40 | homeTeamStats = getStatsForTeam(homeTeam, startDate, endDate, season) 41 | awayTeamStats = getStatsForTeam(awayTeam, startDate, endDate, season) 42 | 43 | currentGame = [homeTeam,awayTeam] 44 | 45 | for stat,statType in availableStats.items(): # Finds Z Score Dif for stats listed above and adds them to list 46 | zScoreDif = zScoreDifferential(homeTeamStats[stat], awayTeamStats[stat], meanDict[stat], standardDeviationDict[stat]) 47 | currentGame.append(zScoreDif) 48 | 49 | if dailyResults[gameNumber] == 'W': # Sets result to 1 if a win 50 | result = 1 51 | else: # Sets result to 0 if loss 52 | result = 0 53 | 54 | currentGame.append(result) 55 | gameNumber += 1 56 | 57 | print(currentGame) 58 | fullDataFrame.append(currentGame) # Adds this list to list of all games on specified date 59 | 60 | return(fullDataFrame) 61 | 62 | 63 | # Function that allows iterating through specified start date to end date 64 | def daterange(startDate, endDate): 65 | 66 | for n in range(int ((endDate - startDate).days)): 67 | yield startDate + timedelta(n) 68 | 69 | 70 | # Returns a list. Index 0 is a dict holding mean for each stat. Index 1 is a dict holding standard deviation for each stat. 71 | def createMeanStandardDeviationDicts(startDate, endDate, season): 72 | 73 | meanDict = {} 74 | standardDeviationDict = {} 75 | 76 | # Loops through and inputs standard deviation and mean for each stat into dict 77 | for stat, statType in availableStats.items(): 78 | statMean = basicOrAdvancedStatMean(startDate, endDate, stat, statType, season) 79 | meanDict.update({stat: statMean}) 80 | 81 | statStandardDeviation = basicOrAdvancedStatStandardDeviation(startDate, endDate, stat, statType, season) 82 | standardDeviationDict.update({stat: statStandardDeviation}) 83 | 84 | bothDicts = [] 85 | bothDicts.append(meanDict) 86 | bothDicts.append(standardDeviationDict) 87 | 88 | return bothDicts 89 | 90 | 91 | # Loops through every date between start and end and appends each game to a singular list to be returned 92 | # season should be in format 'yyyy-yy' and startOfSeason should be in format 'mm/dd/yyyy' 93 | def getTrainingSet(startYear, startMonth, startDay, endYear, endMonth, endDay, season, startOfSeason): 94 | 95 | startDate = date(startYear, startMonth, startDay) 96 | endDate = date(endYear, endMonth, endDay) 97 | 98 | startDateFormatted = startDate.strftime("%m/%d/%Y") # Formats start date in mm/dd/yyyy 99 | allGames = [] 100 | 101 | for singleDate in daterange(startDate, endDate): 102 | currentDate = singleDate.strftime("%m/%d/%Y") # Formats current date in mm/dd/yyyy 103 | print(currentDate) 104 | 105 | previousDay = singleDate - timedelta(days=1) 106 | previousDayFormatted = previousDay.strftime("%m/%d/%Y") 107 | 108 | meanAndStandardDeviationDicts = createMeanStandardDeviationDicts(startOfSeason, previousDayFormatted, season) 109 | meanDict = meanAndStandardDeviationDicts[0] # Dict in format {stat:statMean} 110 | standardDeviationDict = meanAndStandardDeviationDicts[1] # Dict in format {stat:statStDev} 111 | 112 | currentDayGames = dailyMatchupsPast(currentDate, season) # Finds games on current date in loop 113 | currentDayGamesAndStatsList = infoToDataFrame(currentDayGames, meanDict, standardDeviationDict, startOfSeason, previousDayFormatted, season) # Formats Z Score difs for games on current date in loop 114 | 115 | for game in currentDayGamesAndStatsList: # Adds game with stats to list of all games 116 | game.append(currentDate) 117 | allGames.append(game) 118 | 119 | print(allGames) 120 | return(allGames) 121 | 122 | 123 | # Returns a dataframe from list of games with z score differentials 124 | def createDataFrame(listOfGames): 125 | 126 | games = pd.DataFrame( 127 | listOfGames, 128 | columns=['Home', 'Away', 'W_PCT', 'REB', 'TOV', 'PLUS_MINUS', 'OFF_RATING', 'DEF_RATING', 'TS_PCT', 'Result', 'Date'] 129 | ) 130 | 131 | print(games) 132 | return(games) 133 | 134 | 135 | # Creates the logistic regression model and tests accuracy 136 | def performLogReg(dataframe): 137 | 138 | # Update if new stats are added 139 | featureColumns = ['W_PCT', 'REB', 'TOV', 'PLUS_MINUS', 'OFF_RATING', 'DEF_RATING', 'TS_PCT'] 140 | 141 | X = dataframe[featureColumns] # Features 142 | Y = dataframe.Result # Target Variable 143 | 144 | X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.25, shuffle=True) 145 | logreg = LogisticRegression() 146 | 147 | logreg.fit(X_train, Y_train) # Fits model with data 148 | 149 | Y_pred = logreg.predict(X_test) 150 | 151 | confusionMatrix = metrics.confusion_matrix(Y_test, Y_pred) # Diagonals tell you correct predictions 152 | 153 | # Code below prints model accuracy information 154 | print('Coefficient Information:') 155 | 156 | for i in range(len(featureColumns)): # Prints each feature next to its corresponding coefficient in the model 157 | 158 | logregCoefficients = logreg.coef_ 159 | 160 | currentFeature = featureColumns[i] 161 | currentCoefficient = logregCoefficients[0][i] 162 | 163 | print(currentFeature + ': ' + str(currentCoefficient)) 164 | 165 | print('----------------------------------') 166 | 167 | print("Accuracy:", metrics.accuracy_score(Y_test, Y_pred)) 168 | print("Precision:", metrics.precision_score(Y_test, Y_pred)) 169 | print("Recall:", metrics.recall_score(Y_test, Y_pred)) 170 | 171 | print('----------------------------------') 172 | 173 | print('Confusion Matrix:') 174 | print(confusionMatrix) 175 | 176 | return logreg 177 | 178 | 179 | # Saves the model in folder to be used in future 180 | # filename should be end in '.pkl' 181 | def saveModel(model, filename): 182 | 183 | # Change to where you want to save the model 184 | setCurrentWorkingDirectory('SavedModels') 185 | 186 | with open(filename, 'wb') as file: 187 | pickle.dump(model, file) 188 | 189 | 190 | # Used to generate new logistic regression models 191 | # Can import the statistics and predictions for each game from a csv file or can be created on their own 192 | def createModel(startYear=None, startMonth=None, startDay=None, endYear=None, endMonth=None, endDay=None, season='2018-19', startOfSeason = '10/16/2018', filename='model.pkl'): 193 | 194 | # allGames = getTrainingSet(startYear, startMonth, startDay, endYear, endMonth, endDay, season, startOfSeason) # Unnecessary if using data from CSV file 195 | 196 | # allGamesDataframe = createDataFrame(allGames) # Unnecessary if using data from CSV file 197 | 198 | setCurrentWorkingDirectory('Data') 199 | allGamesDataframe = pd.read_csv('COMBINEDgamesWithInfo2016-19.csv') # Should be commented out if needing to obtain data on different range of games 200 | 201 | logRegModel = performLogReg(allGamesDataframe) 202 | 203 | saveModel(logRegModel, filename) 204 | -------------------------------------------------------------------------------- /Data/gamesWithInfo2018-19.csv: -------------------------------------------------------------------------------- 1 | ,Home,Away,W_PCT,REB,TOV,PLUS_MINUS,OFF_RATING,DEF_RATING,TS_PCT,Result,Date 2 | 0,Philadelphia 76ers,Utah Jazz,0.3767003860790348,1.5941648678487557,0.14828686005919234,0.19388745992610967,0.07632353321667118,-0.48476565459642085,0.09866776459423057,1,11/16/2018 3 | 1,Boston Celtics,Toronto Raptors,-1.3692760065412553,0.15182522550940492,-0.8897211603551584,-0.6345407779399951,-2.391470707455716,-2.1121932093129874,-2.2693585856672973,1,11/16/2018 4 | 2,Indiana Pacers,Miami Heat,0.8490707114797298,-1.6700774806034595,-1.4828686005919292,0.45827945073444093,0.17808824417223507,-0.65789624552372,0.49333882297115283,1,11/16/2018 5 | 3,Milwaukee Bucks,Chicago Bulls,2.672778929798868,2.6569414464145957,-0.1482868600591924,3.190330022420531,3.281911928316892,-1.9044365002002361,2.7626974086384504,1,11/16/2018 6 | 4,Minnesota Timberwolves,Portland Trail Blazers,-1.8775225591875717,-3.226286042074866,-0.6672908702663685,-2.3442756518338705,-1.6536765530278905,2.4584543911675807,-1.6280181158047988,1,11/16/2018 7 | 5,Washington Wizards,Brooklyn Nets,-0.25711296192696065,-1.859859012490219,-0.22243029008878856,-0.5992885124988843,-0.5342647325167016,0.7617746000800953,0.7400082344567293,0,11/16/2018 8 | 6,Memphis Grizzlies,Sacramento Kings,0.2630923331345644,-1.2905144168299456,-0.22243029008878856,0.38777491985221924,-0.40705884382224855,-1.1772880183056025,-0.49333882297115283,1,11/16/2018 9 | 7,New Orleans Pelicans,New York Knicks,1.3931934913716695,0.4175193701508655,-0.3707171501479823,1.339586086762212,1.8063236194612329,-0.10387835455637551,2.170690821073067,1,11/16/2018 10 | 8,Chicago Bulls,Toronto Raptors,-2.9920219790076774,-1.535103290672247,0.3727970436883152,-2.616141012221598,-2.884814750416243,1.2073132685772263,-1.971332744341518,0,11/17/2018 11 | 9,Phoenix Suns,Oklahoma City Thunder,-2.5671548579885872,-1.8037463665398912,2.9823763495065228,-2.8475004214656843,-1.7412845790800737,3.1390144983007913,1.010939868893089,0,11/17/2018 12 | 10,Brooklyn Nets,LA Clippers,-1.226729011393148,-0.4605309872016752,-0.14911881747532554,-0.943234514610508,-0.6237437298197255,0.8623666204123045,-1.7691447705629004,0,11/17/2018 13 | 11,Boston Celtics,Utah Jazz,0.7958778464160419,1.4199705438718295,-1.4166287660155983,1.0144220251471499,-0.49379711944061927,-2.6215945260534035,-0.7076579082251624,0,11/17/2018 14 | 12,Orlando Magic,Los Angeles Lakers,-0.6223405716335965,-0.11513274680041743,-1.0438317223272828,-0.9432345146105078,-1.5333700024734989,-0.1379786592659707,-1.6680507836735914,1,11/17/2018 15 | 13,Houston Rockets,Sacramento Kings,-0.19747345061450688,0.07675516453361342,-0.2236782262129883,-0.07118751053664216,-0.02598932207582419,0.10348399444947556,-1.213127842671707,1,11/17/2018 16 | 14,Indiana Pacers,Atlanta Hawks,2.393617583206142,0.19188791133403094,-2.1622228533922287,2.438172235879992,1.8192525453075392,-2.276647877888487,0.9603928754484348,1,11/17/2018 17 | 15,Dallas Mavericks,Golden State Warriors,-1.920878110522929,-0.38377582266806176,1.9385446271792375,-1.1212032909521132,-2.1051350881415796,-0.6898932963298436,-1.7691447705629062,1,11/17/2018 18 | 16,Charlotte Hornets,Philadelphia 76ers,-0.526595868305351,-0.7675516453361235,-2.5350198970805438,0.9254376369763473,1.5073806803976746,0.3449466481649218,-0.1516409803339578,0,11/17/2018 19 | 17,New Orleans Pelicans,Denver Nuggets,-0.8018618903740575,-0.5756637340020927,0.14911881747532554,-1.1567970462204342,-0.025989322075824273,2.1731638834390115,0.40437594755723005,1,11/17/2018 20 | 18,San Antonio Spurs,Golden State Warriors,-1.221093038098119,0.14969852714102228,-2.0119819491421653,-1.393829714008172,-2.0289281135190333,-0.10105714310064623,-3.407122250141817,1,11/18/2018 21 | 19,Orlando Magic,New York Knicks,1.481909026818106,-0.4865202132083145,-1.160758816812788,0.5645892512438166,0.19794420619697806,-0.7747714304382942,0.661083421669309,1,11/18/2018 22 | 20,Miami Heat,Los Angeles Lakers,-0.7883756022672324,1.1227389535576515,1.2381427379336387,-0.1058604846082156,-0.8660059021117822,-0.8758285735389405,-1.0170514179527772,0,11/18/2018 23 | 21,Minnesota Timberwolves,Memphis Grizzlies,-1.215165401990847,1.1601635853429044,-0.38691960560426264,-0.9880311896766788,0.24743025774622351,2.3916857200486534,-0.5593782798740307,0,11/18/2018 24 | 22,Washington Wizards,Portland Trail Blazers,-1.9798304598289898,-4.266408023519075,-0.3869196056042626,-2.1172096921643115,-1.4350954949280956,2.1222000051135903,-0.6102308507716642,0,11/18/2018 25 | 23,Philadelphia 76ers,Phoenix Suns,2.393844843505046,2.2296078389768414,-1.649424368926538,1.9555662262546725,1.4128967318482353,-1.7063116836847314,0.4766421443097869,1,11/19/2018 26 | 24,Detroit Pistons,Cleveland Cavaliers,2.300653803368597,0.6802193407047982,-1.492336333790679,1.338946245003199,0.30276358539605064,-2.1985169770553266,-0.6884830973363674,1,11/19/2018 27 | 25,Indiana Pacers,Utah Jazz,0.7280550010660115,0.0,-0.07854401756793156,0.7751794050018522,0.12615149391502078,-1.4438021938870766,0.15888071476993093,1,11/19/2018 28 | 26,Milwaukee Bucks,Denver Nuggets,0.6290395209210338,0.6802193407047983,0.5498081229755141,0.7223262637517258,0.9082907561881519,-0.3281368622470635,2.0124890537524527,1,11/19/2018 29 | 27,Atlanta Hawks,LA Clippers,-2.7899067640849564,-1.3604386814095992,2.591952579741706,-2.6602747762563563,-3.204247945441529,0.6234600382694226,-2.3832107215489584,0,11/19/2018 30 | 28,Sacramento Kings,Oklahoma City Thunder,-0.9726814814241915,-1.4738085715270646,0.23563205270379056,-1.3741816725032834,-0.25230298783004157,2.1657032908306175,1.2180854799028038,1,11/19/2018 31 | 29,Charlotte Hornets,Boston Celtics,-0.5591462408186965,-0.8313791941947557,-0.7854401756793046,0.29950113375071563,2.043654201423336,2.132889604605913,1.2710457181594474,1,11/19/2018 32 | 30,Memphis Grizzlies,Dallas Mavericks,1.1648880017056185,-1.6249684250170195,-2.3563205270379126,0.19379485125046314,-0.5298362744430859,-1.0500379591906042,-1.0062445268762232,1,11/19/2018 33 | 31,New Orleans Pelicans,San Antonio Spurs,0.1747332002558423,0.11336989011746545,1.727968386494471,0.33473656125079976,1.1858240428011964,0.9844105867411906,1.800648100725878,1,11/19/2018 34 | 32,New York Knicks,Portland Trail Blazers,-2.5748643822408566,-2.4011686065101365,0.1675679221107877,-2.394852307795513,-1.8333835219909447,2.0293159936048752,-2.25258232823776,0,11/20/2018 35 | 33,Orlando Magic,Toronto Raptors,-1.341430450792146,-1.1052998347427612,-0.8378396105539341,-1.809443965889943,-1.5493381875979826,1.127397774224931,-1.9229361338615014,0,11/20/2018 36 | 34,Miami Heat,Brooklyn Nets,-0.21030901135300578,0.9147308977181472,1.7594631821632625,0.017739646724411195,-0.877958306305525,-0.9985523143135087,-0.16482309718812926,0,11/20/2018 37 | 35,Washington Wizards,LA Clippers,-2.1315102501993843,-2.0962583072707552,-0.0837839610553931,-1.8626629060631767,-1.6784497032311478,1.3850886940477762,-1.3735258099010708,1,11/20/2018 38 | 36,Philadelphia 76ers,New Orleans Pelicans,0.2508464967526054,0.23200708749513732,1.5079896290386747,-0.41282257807726536,-1.6891741413670194,-1.4176678398576426,-0.5464155496331141,1,11/21/2018 39 | 37,Golden State Warriors,Oklahoma City Thunder,0.23944438326385054,-0.425346327074418,0.4188860080662984,0.23333450065236738,1.8191106137798667,1.8043045234551833,3.442417962688613,0,11/21/2018 40 | 38,San Antonio Spurs,Memphis Grizzlies,-1.0717986679429488,2.010728091624517,-1.5917668306519341,-0.7179523096995919,0.6756696565468099,2.0942820361533396,-0.8196233244496711,0,11/21/2018 41 | 39,Chicago Bulls,Phoenix Suns,0.2679496669857371,0.4253463270744178,-1.9268756371049733,0.07179523096995921,-0.285860239308267,-0.3544169599644095,-1.2567557641561624,1,11/21/2018 42 | 40,Boston Celtics,New York Knicks,1.7502244205238588,0.6186855666536959,-1.2566580241988952,1.6153926968240817,-0.4677713006862508,-3.383070981478472,0.21856621985324565,0,11/21/2018 43 | 41,Cleveland Cavaliers,Los Angeles Lakers,-2.4514544000822767,0.23200708749513457,1.0891036209723763,-1.7769319665064898,-1.5852249634367435,1.321008668958263,-1.475321984009402,0,11/21/2018 44 | 42,Houston Rockets,Detroit Pistons,-0.21664015628634034,-1.817388852045239,0.4188860080662984,-0.03589761548497959,0.4937585951688225,0.7088339199288191,1.2567557641561624,1,11/21/2018 45 | 43,Utah Jazz,Sacramento Kings,-0.33066129117388887,-0.30934278332684795,0.9215492177458561,-0.07179523096995921,-0.3118475337908314,-0.12887889453251503,-0.0546415549633114,0,11/21/2018 46 | 44,Milwaukee Bucks,Portland Trail Blazers,0.2508464967526052,-0.4640141749902744,0.4188860080662984,0.7179523096995919,0.44178400620368263,-0.7410536435619546,1.2567557641561624,1,11/21/2018 47 | 45,Atlanta Hawks,Toronto Raptors,-3.4320361601151887,-1.5080460687183883,2.094430040331492,-3.230785393648164,-3.040513454460635,1.7398650761889258,-2.2403037534957617,0,11/21/2018 48 | 46,Dallas Mavericks,Brooklyn Nets,-0.03420634046626436,-0.23200708749513732,2.0944300403314933,0.2512833083948572,-0.5457331841339585,-0.966591708993849,1.0381895443029108,1,11/21/2018 49 | 47,Minnesota Timberwolves,Denver Nuggets,-1.0033859870104205,-1.546713916634245,-0.5026632096795577,-1.8487271974764492,-1.1694282517156287,1.9009636943545718,-0.8196233244496712,0,11/21/2018 50 | 48,Charlotte Hornets,Indiana Pacers,-0.8380553414234764,0.5026820229061285,-1.8430984354917137,-0.07179523096995921,1.117453662750489,1.6109861816564153,0.0,1,11/21/2018 51 | 49,Philadelphia 76ers,Cleveland Cavaliers,3.01337197284278,0.6142347586793641,0.6213014000878876,1.5872755965894196,0.728522672564075,-2.0691004086847427,1.7498155724145006,0,11/23/2018 52 | 50,Golden State Warriors,Portland Trail Blazers,-0.20089146485618548,-1.765924931203171,0.08875734286969778,0.0547336412617041,0.45869946050330535,0.4926429544487485,1.5804785815356834,1,11/23/2018 53 | 51,Los Angeles Lakers,Utah Jazz,0.8265248839797337,0.7294037759317464,-0.6213014000878876,0.7845155247510924,1.268169096685618,-0.032842863629919394,0.5644566362627379,1,11/23/2018 54 | 52,Chicago Bulls,Miami Heat,-0.43048171040611116,-2.418549362299996,-0.7988160858272846,-1.3318519373681337,-1.0523105270369995,1.1495002270470798,0.3386739817576464,0,11/23/2018 55 | 53,New York Knicks,New Orleans Pelicans,-1.6817485486532089,-0.5758450862619033,0.798816085827283,-1.4960528611532462,-1.915744805631467,0.2299000454094169,-1.975598226919598,1,11/23/2018 56 | 54,Brooklyn Nets,Minnesota Timberwolves,0.18367219643994065,0.6526244310968221,0.0,0.6020700538787455,0.9173989210066185,-0.2299000454094169,0.2257826545050977,0,11/23/2018 57 | 55,LA Clippers,Memphis Grizzlies,-0.33864561218614053,1.6891455863682494,0.5325440572181881,0.21893456504681652,1.7538508783950029,1.7735146360154965,1.4675872542831285,1,11/23/2018 58 | 56,Detroit Pistons,Houston Rockets,-0.1721926841624441,1.6891455863682494,-0.35502937147879265,0.0,-0.51266410291546,-0.624014408968412,-1.4675872542831347,1,11/23/2018 59 | 57,Indiana Pacers,San Antonio Spurs,0.8035658594247413,-1.1900798449412675,2.396448257481849,0.8210046189255619,-0.16189392723646026,-1.6421431814958285,1.2982502634043054,0,11/23/2018 60 | 58,Atlanta Hawks,Boston Celtics,-1.9113387942031346,-1.8427042760380896,3.2840216861788303,-2.2440792917298693,-0.971363563418773,2.6602719540232447,0.11289132725254891,0,11/23/2018 61 | 59,Milwaukee Bucks,Phoenix Suns,3.3807163657226615,3.9157465865809415,-2.041418886003055,4.141512188802279,3.6426133628203905,-2.9887005903224058,2.1449352177984213,0,11/23/2018 62 | 60,Denver Nuggets,Orlando Magic,0.6371129314010447,1.343638534611108,0.7100587429575853,1.4413192198915419,1.1332574906552333,-1.0509716361573311,0.7337936271415674,1,11/23/2018 63 | 61,Toronto Raptors,Washington Wizards,2.5025336764941946,2.456939034717454,1.2426028001757736,2.4082802155149814,1.5379923087463878,-2.3975290449839086,1.4675872542831285,1,11/23/2018 64 | 62,Oklahoma City Thunder,Charlotte Hornets,0.677291224372282,1.3052488621936473,1.1538454573060757,0.12771182961064298,-1.2951514178916936,-1.8720432269052452,-1.8627068996670495,1,11/23/2018 65 | 63,Golden State Warriors,Sacramento Kings,0.5724682356101753,0.634934310952399,0.36890603998955573,1.2815099121626126,1.702124007743444,-0.09760271077929442,2.064004257705127,1,11/24/2018 66 | 64,Cleveland Cavaliers,Houston Rockets,-2.1498009273445957,1.0317682552976482,0.09222650999738893,-1.2815099121626128,-0.8645709245680997,1.3013694770572715,-1.089335580455487,1,11/24/2018 67 | 65,Milwaukee Bucks,San Antonio 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