Predicting basketball RPI

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Predicting basketball RPI. What is RPI?. R atings P ercentage I ndex Based on win/loss percentage throughout the season. Not necessarily a predictor of a stronger team. How is RPI Calculated?. Weighted wins, losses Wins worth 1.4 away, 1 neutral, .6 at home
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Predicting basketball RPIWhat is RPI?
  • Ratings Percentage Index
  • Based on win/loss percentage throughout the season.
  • Not necessarily a predictor of a stronger team.
  • How is RPI Calculated?
  • Weighted wins, losses
  • Wins worth 1.4 away, 1 neutral, .6 at home
  • Losses worth 1.4 at home, 1 neutral, .6 away
  • Two parts:
  • Win pct (wins/(wins+losses)
  • Strength of schedule
  • Opponents unweighted win pct
  • Opponents’ opponents unweighted win pct
  • What is RPI used for?
  • Estimator of team strength, as it factors in strength of schedule
  • Helps to seed the NCAA tournament.
  • Helps selection committee/analysts determine quality of wins.
  • The Selection Committee
  • 10-person committee that determines who will receive an at-large bid and seeding for the tournament
  • 5 year Tenure
  • Use multitude of selection tools
  • Win/loss
  • Conference strength
  • How a team won
  • Voodoo
  • Apparently, more random numbers than me.
  • Decidedly NOT just RPI.
  • How can RPI be predicted?
  • Predict outcomes of games
  • Run through season
  • Rinse, repeat (Monte-Carlo!)
  • Kenpom statistics
  • Statistics on all division 1 basketball teams
  • Offensive Efficiency
  • Defensive Efficiency
  • Tempo
  • Average possesions per game: FGA-OR+TO+.42 FTA
  • Meteorologist from Salt Lake City, Utah
  • Basketball stats just a hobby, no background
  • Stats referenced by ESPN, wall street journal
  • Season averages
  • Step 1: Predicting games
  • Generate scores: compare and mark
  • Home games:
  • xscore<-round(x[8]/100*rnorm(1,1.05,.2)*(((x[4]+y[4])/2)*rnorm(1,1.1,.2))+((y[12]*rnorm(1,1.05,.2))/80))
  • yscore<-round(y[8]/100*rnorm(1,.95,.2)*(((y[4]+x[4])/2)*rnorm(1,.9,.2))+((x[12]*rnorm(1,.95,.2))/80))
  • Step 2: Recording data
  • Compare scores
  • Higher score wins
  • Mark wins/losses in appropriate places
  • Step 3: Run through season
  • 5284 division 1 vs division 1 games.
  • Import list of all games, which team is home, away, to be called and put into game function.
  • Run 1 of three game situations based on court (1 is home, 2 is away, 3 is neutral).
  • Each team plays approximately 30 games.
  • Step 4: Compile RPI, rank, repeat!
  • After season is done, run through game list to grab opponent’s win-losses.
  • Next, re-run through game list to grab opponent’s opponent’s win-losses by.
  • What do these results mean?
  • Interesting estimator, but cannot be taken too seriously.
  • True Top 25 RPI missed by average of 13 places.
  • My top 25 missed true RPI by average of 11.
  • Kansas, WVU right where they should be!
  • Villanova ranked 18, therefore project should be considered a success.
  • Is the RPI reliable?
  • [252]Wake Forest(7.4%), [202]DePaul (9.9%) more likely to make tournament than nearly 200 other teams based on RPI alone.
  • Too much weight placed on who you play, not how you play.
  • Still only one factor in determining NCAA tournament.
  • Interesting Oddities
  • Program took over 15 hours to run.
  • In 10,000 simulated seasons, 31 teams will not receive an at-large bid (will not be in the top 37 RPI at the end of the season)
  • Of those 31 teams, half of them would likely end up as a play-in team going to the final four.
  • VCU received at large bid with RPI rank of 49 (to fill 36th-38th at large bid), Harvard with RPI rank of 35 denied tournament bid
  • Are these results reliable?
  • Maybe?
  • Only 2 teams predicted correctly in top 25.
  • Season averages inaccurate for day-of play, but might average out over whole season.
  • Effect of random variables should eventually absorb things like suspensions, injuries, team morale.
  • Possible that one of the seasons actually matches this season perfectly.
  • Theoretical/Technical Issues
  • Unable to account for mid-season tournaments, changes in schedule, delayed games.
  • ‘Labor Intensive’ program – 5 trillion calculations.
  • Still near-impossible to seed mock tournament without just taking 68 highest RPI (which might not be a bad idea)
  • March Madness
  • There are about 14,757,395,260,000,000,000 different brackets of the NCAA tournament (but only 1 winner!)
  • Over 6 million brackets were submitted to ESPN.com this march in competition.
  • Of those brackets, the best bracket, just 1 of 6 million, got 52, or 77.6% of their picks correct.
  • This year was the first time 2 11 seeds made the sweet 16, and the first time no 1 or 2 seeds made the final 4.
  • Most even field the tournament has ever had, no great teams
  • My terrible bracket
  • My original bracket:
  • 33.6th percentile on ESPN at 480 pts (4-millionth place) Champion: Notre Dame
  • No final four team, only 2 elite 8 (UNC, Uconn)
  • Basically, terrible. Last place in every pool I was in.Can I make my bracket any better?
  • This year, no, but next year? Maybe!
  • Goal: create a bracket based on Kenpom rankings, and see if it does any better.
  • Results:
  • Worth twice as many points on ESPN, enough to put me in the 92nd percentile!
  • Beat 5,520,000 brackets!
  • Still had no final 4 team.
  • Is it a reliable method? Votes for no:
  • Highest championship percentage was less than 6%, only 4% better than flipping a coin.
  • Many games were decided by less than a percentage point
  • Doesn’t take into account injuries, coaching, stage fright, ‘home field,’ streaks, incredible ability to lose the lead, or recruiting violations. Examples:
  • Georgetown and St. Johns both had their star players hurt going into the tournament and lost in the first round.
  • Tennessee head coach Bruce Pearl was hit with school and NCAA sanctions the day before the game and lost by 30 points.
  • George Mason entered the tournament on an 11 game wn-streak
  • Votes for Yes:
  • Randomness exists to account for the issues previously mentioned.
  • Anything can happen, this season could have been that 1 in 10,000 chance for VCU, data could be reliable.
  • 55.2% of the bracket picked correctly, up from… well, zero-ish.
  • Oddities and anecdotes
  • First trial of the tournament I ran (before looping) yielded Butler over Uconn, with Kentucky and Kansas in the final 4.
  • Defeated teams sometimes more likely to advance: Notre Dame has a higher chance of making the championship game over Wisconsin, 8.47% over 8.38%, but Wisconsin is more likely to win the championship, 4.90% over 4.76%
  • VCU had only a 51.5% chance of winning it’s first game, a 22% chance of advancing past Georgetown, and a 2.64% chance of advancing to the final 4.
  • Conclusions: RPI
  • Can RPI (remember RPI?) be predicted for a season using Monte-Carlo methods?
  • Decent yardstick, but not perfect
  • Since RPI is just a yardstick anyway, should work okay.
  • Can it be used to seed a tournament?
  • Difficult but yes, would need to run through 31 conference tournaments and determine an Ivy league AQ as well.
  • Conclusions: NCAA tournament
  • Can Monte-Carlo methods be used to predict the NCAA tournament?
  • Better predictor than me and better than a coin flip.
  • Good for calculating odds but not for absolute winner.
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