3-point shooting percentage projection model

During the application/interview process with the Philadelphia 76ers front office, I was presented the project of predicting three-point shooting percentages for all NBA players this season.  From my general awareness of statistical projection systems for baseball, the basis of the model would be to use past historical data to estimate a player’s true skill level.  However, there are additional factors that could influence a player’s percentages.  For example, a player’s true skill level can evolve over time, and while the direction and extent of that change may vary significantly by the player, there could be some generic trend evident across basketball.  In addition, a player’s shooting percentages can depend heavily on the intra-game context of his shots, such as the location of the shots (distance or location along the arc), how open he is, and whether the shots are off-the-dribble or catch-and-shoot.  Furthermore, there may be subtle inter-game influences such as whether the games occur at home or on the road and how much travel and rest time the player has had.  Of the many different variables that could in theory impact three-point shooting percentages, many of them are either themselves unknown or their average effects may be determined to be minimal.  As a result, the goal of this project was to build the model foundation that can predict three-point shooting percentages on its own and that can be extended in the future to include additional variables.

2014 NBA Playoffs Finals Preview

We're adding smaller and smaller sample sizes as the playoffs progress, so my overall results won't change much.  I am now 28-21 overall after going 3-2 in the Conference Finals:

SAS 5 times: 3-2-0

2014 NBA Draft Big Board 1.0

This is the first version of my attempt at creating a 2014 NBA Draft Big Board. First, here are some of its guiding principles:

2014 NBA Playoffs Conference Finals Preview

After an excellent first round, my model had a much less successful second round, as I was 5-5-0 against the spread using my model with subjective adjustments, making me 25-19 for the entire playoffs.  Here are the full results:

LAC 6 times: 2-4-0
WAS 3 times: 3-1-0

Granted, this was an extremely small sample size, and adding my second-round results to my first-round results doesn't significantly increase the likelihood a coin-flip strategy would match my record.  The bigger issue is the 2-4 record betting on the Clippers given the confidence I had in that bet.  Specifically, a 57%-weighted coin would be just as likely to finish with a record as poor as 2-4 as a fair coin would be to finish with a record as good as 25-19.

2014 NBA Playoffs 2nd Round Preview

There are many potential areas of improvement in both the model and the testing of the model, most importantly perhaps a means of adjusting for various series states (i.e. how should the line be adjusted when the home team is down 1-0), but in the still small sample size of the first round, I was 20-14-1 against the spread using my model with subjective adjustments.  While that's a 59% win percentage (which would be amazing if it were indeed my true win percentage), the small sample size means that this likely isn't my true win percentage, as even a coin flip would still finish with the same record or better in 34 trials 20% of the time.  Here are the full results:

ATL 6 times: 3-2-1
MIA 3 times: 2-1-0
BKN 7 times: 4-3-0
CHI 2 tims: 1-1-0
DAL 1 time: 1-0-0
SAS 1 time: 0-1-0
MEM 7 times : 4-3-0
GSW 7 times: 5-2-0
HOU 1 time: 0-1-0

2014 NBA Playoffs 1st Round Preview

This is my first attempt at building a simple model for estimating NBA playoff series.  The idea was inspired by colts18 on the APBR forums.  The model is based off of the xRAPM numbers on ESPN (where it's called "real plus minus"), which are supposedly the most predictive of the all-in-one metrics for NBA games.  I utilized my own minutes estimates for each series (subjectively based on regular season minutes, an increase  for each team's top players in the playoffs, and potential matchup adjustments I expect each coach to employ) to calculate each team's offensive and defensive rating (the league average points per possession is set to 104 by weighting each team's offensive/defensive rating by each team's pace).  Then, I assumed 100 possessions per game (which is most definitely not true) and a home court advantage of four points (divided evenly between offensive and defense, so 1 point per team per side of the ball) to calculate each team's Pythagorean win percentage at home and on the road, and used Bill James' Log 5 formula for estimating a matchup based on each team's win%.  Finally, assuming games are independent (which, again, is also not true), each permutation was considered to calculate a team's series win percentage.  All of the computation was done in R and the script I ran will be included as well.  I will then comment on any other subjective observations for each series.

Tanking in the NBA

People disagree about the significance of the tanking problem in the NBA, but no one doubts that it exists.  Most of the media coverage on tanking has focused only on the race for draft lottery ping-pong balls that was especially evident this year, given the expected strength of the incoming draft class and the projected gap between the top teams and the bottom teams before the season even began.  This kind of tanking can manifest in many different forms and degrees, with some front offices actively trading away productive players (Boston trading Pierce, Garnett, Lee, and Crawford or Philadelphia trading Turner and Hawes), others benching players towards the end of the year citing bogus injuries (Milwaukee holding Sanders out until it was beneficial to medically clear him to start his marijuana suspension), and others simply making no effort to improve the team at any point in the season (Philadelphia not bothering to reach the salary floor or Utah trading for Jefferson and Biedrins to reach the salary floor).  Still, this might not even be the most egregious manner by which teams actively trying to lose games, as many of these draft lottery tankers initially tried to compete and arguably only Philadelphia, Utah, and Boston stuck to season-long losing blueprints.  There are two rules that even more directly incentive teams to intentionally lose, and each of these is more easily fixable.