Statistics

With or Without You: The Bulls

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A couple of months ago, I introduced my method of With-or-Without-You(WOWY) for the NBA. This time, I’ll revise and expand upon the method, and take a pre-playoff look at the hottest team going: the Chicago Bulls.

As Kevin Pelton chronicled, the Bulls have actually been quite healthy this year–only Joakim Noah and Carlos Boozer have missed significant time, among the regulars in the rotation. Kurt Thomas has missed time, also, but he has also been sat by Tom Thibodeau when healthy–complicating any analysis of WOWY. For this analysis, I’ll focus on Noah and Boozer.

Bayesian Golf Ratings and Masters Preview

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That’s right, golf.  I’m taking up where Ken Pomeroy left off.  A year or two ago, he developed a rating system for golfers–basically, he created a huge regression of all players and all specific rounds at tournaments.  Each round was assigned a level of difficulty, and each player was assigned an overall rating.  His numbers, … [Read more…]

Final 4 Update

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VCU and Butler have continued their run.  Before the tournament, I had them highly because they elevated their games in the regular season when playing good opponents.  They have both continued that and increased that trend. One confounding issue now with adding the “Raising their Game” (“Ag25” in the table below) bonus is that for … [Read more…]

Sweet 16 Update

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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…]

On Crowds and Contrarian Picks

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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…]

NCAA Tourney Bayesian Ratings and Odds

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Okay, ready for the tournament? I’ve put together some adjustments based on the work I .  The theory behind the adjustments may be found on STATS @ MTSU and Dr. Winner @ Florida.  Basically, I’m adjusting for teams that raise their game to a higher level against good foes (or vise-versa).  For instance, Long Island … [Read more…]

Raising Their Game

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When we get to the NCAA tournament, it seems that inevitably, some teams will raise their game, matching up with the “better” teams, suddenly emerging as a top team.  Some teams play well when their opponent is better, and let the foot off the gas when playing East Popcorn St.  Those teams tend to be … [Read more…]

How Does the Committee Seed? Introducing ExpSd

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The Bracket was revealed yesterday.  Quick thoughts and long ramblings below: I have two rankings systems: my Bayesian predictive power ratings, which tell how good the teams are, and my DSMRPI ratings, which tell how much they have accomplished.  I would put teams in and seed them based on DSMRPI, which looks purely at win-loss … [Read more…]

NCAA Bayesian Ratings, With Projection Prior

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In my , I took as the Bayesian prior the overall distribution of NCAA teams.  Now, we know more than that–we can create a pretty good projection of how good a team will be based on how good the team has been the previous few years.  So let’s do it! I compiled the Pomeroy Ratings … [Read more…]

NCAA Bayesian Analysis & DSMRPI

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 I posted my NCAA Bayesian Ratings and methodology.  Today I thought I’d update the numbers quickly and add a new twist. What is the objective in basketball? To win the game! When doing a predictive rating system (like this Bayesian method) or even trying to tell how good teams are over this season (KenPom’s ratings), … [Read more…]