Here are my 2013 NBA Team Projections, based on my ASPM player projections. For playing time, I roughly used ESPN’s Fantasy Projections, tweaked so each team totaled 475% of the minutes (I assumed the rest of the minutes would go to replacement level (-3.25) players). Here are my regular season projections: I have updated these … [Read more…]
The NFL season begins tonight, and there are predictions flying everywhere. I thought I’d take a basic analytical approach and produce a baseline projection. If your predictions can’t beat this (dumb) system, you aren’t very good at predictions! What got me started down this trail was Bill Barnwell’s article on Grantland breaking down some good … [Read more…]
Quickly (Updated WOWY Bayesian Ratings, roughly done due to lack of time:) CHI ATL MIA BOS OKC MEM LAL DAL 98.4% 1.6% 69.9% 30.1% 67.1% 32.9% 52.3% 47.7% CHI vs ATL MIA vs BOS OKC vs MEM LAL vs DAL 4 to 0 45.3% 4 to 0 10.3% 4 to 0 9.1% 4 to 0 … [Read more…]
Here are my Bayesian WOWY + Tightened Rotations NBA projections for 2011! (Note: I’d still bump the Lakers up, personally… and I’m going to pick OKC>DEN for matchup and homerism reasons) Seed Tm WOWY Shortened Combined ReStdev Win 1st Win 2nd Win Conf Win Title 1 CHI 10.40 0.16 10.56 11.9 96.85% 67.32% 44.34% 31.75% … [Read more…]
After a ton of math and fudging, I’ve come up with these estimates for how much a “tightened rotation” will help each team in the playoffs. I took close games between playoff teams in the past 1.5 months, figured out the top rotation (MPG/player) for each team when everyone’s healthy, and took the max of … [Read more…]
There is a ! I’m putting together an NBA Playoffs preview; this is the first of the data for that: Here is a table of playoff bound teams and their season-long adjusted efficiency ratings. Ratings are adjusted for location, pace (obviously, since these are per 100 possessions), opponent (recursively) and rest day situation. Previous is … [Read more…]
Well, the first weekend of the NCAA Tournament is in the books. I don’t have much time to post, so I’ll make it quick: Predictions did fairly well. In the 8 closest 1st-round games by my pregame predictions (averaging a 52.72% favorite) my stats went only 25%–otherwise, everything matched up really well. The other 3 … [Read more…]
If you are choosing NCAA tournament picks in a LARGE group (like ESPN), then, if possible, you need to account for what the masses have chosen in making your own selections. Fortunately, ESPN publicly shows what everyone has picked–and that lets us account for them. As the number of people approaches infinity, the formula for … [Read more…]
Carmelo Anthony was FINALLY traded yesterday, in a mega 3-team deal. How did the teams make out? There are several good trade analyses around, but none of them are really focusing on the financial aspect. Kevin Pelton’s article is a good primer on the trade as a starting point, and Joe Treutlein at Hoopdata has … [Read more…]
A few days ago, I put up a massive post on . Nathan Walker (the Basketball Distribution) commented that I should apply the system to NCAA basketball, and so I have. Thanks to Ken Pomeroy’s incredible NCAA basketball database, the data was quite easy to obtain. Since he already compiles a fully adjusted efficiency rating … [Read more…]