The Big 12 tourney starts in a couple days. Here’s the schedule if you haven’t already seen it. Now let’s get right to the numbers. First, here’s how all the teams have been playing over the last 10 games:

stats glossary

Hey, Colorado’s 1st in something? What the… oh, tempo. OK. Looking at the Pythagorean rating, the top 3 teams here are no surprise. What might surprise you is that if I made this same chart for ALL of Division I NCAA basketball, those three would be ranked 1st, 5th, and 8th. Texas has officially joined the party. Even more surprising is that all three are top 5 in offense. I haven’t actually run the numbers on ALL teams, so the ranks in this next table are where they rate among the 78 that I have looked at. Any team with an outside shot at a 12-seed or better in the big dance was included, along with all Big 12 teams.

I’d be shocked if somebody outside that group cuts down the nets in Oklahoma City. That said, if it happens, it’s bound to be a team out of the KU side of the bracket. Any dark horse from the other side will have to take down KU, A&M, and Texas. Sorry, but nobody’s winning a tournament by beating all three top-10 rated Big 12 powers in consecutive games. That’s impossible.

I’m talking a lot about the top seeds here because I’m not going to have time to do individual game previews of the later rounds. Don’t worry, we’ll get to everyone else right after this page break. (more…)

These were done, as always, with data from www.kenpom.com.  Each column is the team’s chance of advancing to that round.  They take into account the fact that these games are being played at the Ford Center in Oklahoma City- all Ok. St.’s games are considered “semi-home”.

A couple specifics of interest- Kansas beats Texas A&M 57.5% of the time, and beats Texas 77.9% of the time.  They beat Colorado 98.9% of the time by an average of 29.2 points (although that may or may not be of interest).

Click here for the preview that actually gives you some ideas about how this game’s going to be played. Read on for the one with pretty graphs and hand waving. Format is the same as last time, so you can skip the next paragraph unless you need a refresher. (And for reference, here is the original post that kind of explains what I’m doing).

After the break, for both teams I’ve included a graph that charts the offensive and defensive ratings for each game of the season. Keep in mind that for the defensive rating, lower is better. For both offense and defense, I’ve included a trendline showing roughly how each unit has progressed over the year. The dotted line shows the national average efficiency. I’ve also included the average ratings for their last ten games, to give a snapshot of how the team is playing right now. To give these numbers some context, I show where this would rank in the full-season stats, and what team’s full-season rating is the closest. (more…)

Here’s a glossary of terms thrown around on Phog Blog:

(Estimated) Team Possessions = FGA+(.44*FTA)+TO-OR

THE FOUR FACTORS

Effective Field Goal Perentage (eFG%) = (FGM+(0.5*3PTM))/FGA

Free Throw Rate (FT Rate) = (FTM*100)/FGA

Note: For teams, their opponents’ FT Rate is calculated as (FTA*100)/FGA on the assumption that, over time, you have minimal control over how well your opponents shoot free throws.

Turnover Percentage (TO%) = TO/Possessions

Offensive Rebounding Percentage (OR%) = OR/(teamOR+oppDR)

Note: individual offensive rebounding percentage = player’s OR/((teamOR+oppDR)*(player’s MIN/(teamMIN/5)))

or

Defensive Rebounding Percentage (DR%) = DR/(teamDR+oppOR)

Note: individual defensive rebounding percentage = player’s DR/((teamDR+oppOR)*(player’s MIN/(teamMIN/5)))

SOME INDIVIDUAL STATS

PPWS (Points Per Weighted Shot) = PTS/(FGA+(.44*FTA))
Pts/100 = Points per 100 individual possessions
A/100 = Assists per 100 individual possessions
TO/100 = Turnovers per 100 individual possessions
S/100 = Steals per 100 individual possessions
BS/100 = Blocked Shots per 100 individual possessions

Note: individual possessions = (Min/(teamMin/5))*teamPossessions

TEAM EFFICIENCY STATS

Offensive Efficiency (OE) = the number of points a team scores per 100 possessions
Defensive Efficiency (DE) = the number of points a team allows per 100 possessions

Efficiency ratings come in several flavors. If one of these words is used to describe an efficiency rating, it means the following:

Raw (OE or DE)= tells you what actually happened, without adjusting for the opponent or location
Adjusted = adjusted based on opponent rating and location in order to indicate how many points a team would score or allow per 100 possessions against an exactly average opponent. There are two types of adjustments.

• Season (AOE or ADE) = Explained in depth here. Essentially, all the ratings for all the teams are adjusted so that predictions based on the ratings best match the actual results.
• Single game (AGOE or AGDE) = Takes the opponent ratings, location, and score from a single game and tells you the ratings of a hypothetical team that would have gotten the same result.

Pythagorean Rating (Pyth) = a rating that supposedly tells what a team’s winning percentage would be over time against an average schedule. As always, a longer explanation is on Pomeroy’s site. The formula is:

AOE^11.5 / (AOE^11.5 + ADE^11.5) … AGOE and AGDE can be used in place of AOE and ADE

SCORE PREDICTIONS

Predictions based on efficiency ratings all use the same formula, listed below. More explanation can be found here. The only difference between the various predictions is in what values are used for the offensive and defensive efficiencies.

[TeamA predicted offensive efficiency] = [TeamA offensive efficiency] x [TeamB defensive efficiency] / [National average efficiency]

Home field advantage is accounted for by multiplying the home offense and the visiting defense by 1.014, and dividing the home defense and the visiting offense by the same.

“Last 10″ = uses the average of the two teams’ last 10 Adjusted Game Efficieny ratings
Trendline = A 2nd-order polynomial trendline is fit to the season-long graph of individual AGOE and AGDE ratings. The trendline’s value as of the latest game is used in the equation.
Streaks = This tries to take a middle ground between two “streak” predicitons. The last N AGOE and AGDE ratings are averaged, where N is between 5 and 10, with the value of N selected to maximize the home team’s predicted margin of victory. Then the reverse is done, but N is selected to maximize the visiting team’s margin of victory. The two predictions are then averaged.Recommended further reading for those interested is available from the invaluable Ken Pomeroy (some of these are the same links from above):

• Efficiency ratings
• Pythagorean Rating
• The Four Factors
• Miscellaneous

If any of the above is unclear or incomplete, please ask about it in the comments and we’ll be glad to try and do better.

Welcome visitors from Sports Illustrated. If you like what you see, please add Phog Blog to your favorites and tell your friends.

I posted an efficiency laden preview of the Ohio St vs. Wisconsin game over at yocohoops. I’m going to do the same thing here for the KU-OU game, but with less explanation of the numbers, since you PB readers have had a couple posts to get used to them. For reference, here is the original post that explains what I’m doing. There’s not going to be a lot of analysis, just numbers and graphs. Sorry about that, but I feel Hoopinion and Chalmersfan do a much better job of that than I do.

After the break, for both teams I’ve included a graph that charts the offensive and defensive ratings for each game of the season. Keep in mind that for the defensive rating, lower is better. For both offense and defense, I’ve included a trendline showing roughly how each unit has progressed over the year. Also, the dotted line shows the national average efficiency.

I’ve also included the average ratings for their last ten games, to give a snapshot of how the team is playing right now. To give these numbers some context, I show where this would rank in the full-season stats, and what team’s full-season rating is the closest. (more…)

Recently I posted some game-by-game adjusted efficiency ratings for Kansas, derived from Ken Pomeroy’s Game Plan and season efficiency ratings. The Hawks’ numbers looked good, but Jeremy asked for some context on how the numbers were changing as the season progressed, and how this compared to other top teams. So I ran the game-by-game numbers for Pomeroy’s top 11 teams. (Why top 11? I’ll explain Michigan State’s case later on.) Just showing you a mess o’ single game numbers doesn’t do a whole lot of good - there’s a lot of game to game variation. To smooth that noise out and get a better idea of a team’s general trend, we can look at a moving 10-game snapshot.

Graphs after the jump… (more…)

It has seemed to me like Kansas has gotten their act together a little since the Texas Tech loss. They’ve stopped letting inferior teams hang around, they’ve pushed the tempo, and their offense seems to have benefited. I wanted to see what the stats said about this, so I played with Pomeroy’s.

His Game Plan pages list raw game-by-game efficiency stats on offense and defense. These tell you only half of the story, though. You can see a team’s output, but you need the context of that output. 1.1 points per possession against Texas A&M is excellent. Against Baylor, eh. To get that context, you need the opponent’s seasonal adjusted efficiency stats. Taking those two pieces of the puzzle, I worked backwards from the formulas and parameters Pomeroy lists on his site to obtain adjusted game-by-game efficiency stats.

So, was my perception correct? Has KU’s offense been on an uptick? Here are the game-by-game adjusted efficiency numbers since the Texas Tech loss. “Equiv Rnk” indicates what their Pomeroy Rating rank would be if they played like that every game… OR what rank team they could be expected to beat on a neutral court if they performed at that level.

The offense has indeed taken a step forward. A full season Adj Off Eff of 125.1 would rank 2nd in the country (behind Georgetown). The defense has slipped a little - 84.6 would only rank 6th. One caveat, though - the one game where they needed to play like a top-5 team (vs. Texas A&M), they didn’t.

One other caveat - I cherry-picked this data to show only their recent warm streak. How do the Hawks stack up if I do the same for the other top teams? For each team, I used their most recent 5 to 10 games, whatever gave them the best results. I looked at the top 10 and a few other teams I suspected might be able to crack the top, so there’s a chance I missed some team on a ridiculous tear. But I think these are the 10 hottest teams. “.99+” is the number of .99+ games in the past 10.

Three things jump out at me:

• Georgetown’s offense is unstoppable.
• If not for the letdown against NC St, I’d be calling UNC’s defense unbreakable.
• It’s UNC and everyone else.

I’m sure I’ll mess around with this sort of thing more as we get closer to the tournament. For now it’s nice to see that KU can get as hot as any almost team in the country.