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 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.