You may have noticed that the ratings pages look a little bit different than they used to. The Pyth column has been changed to AdjEM (adjusted efficiency margin) and likewise, strength of schedule and conference strength measures have been converted to the new scale.
The main benefit of the change is that the team rating means something that is more easily understood by humans. The problem with the “Pythagorean winning percentage” was (a) it was a mouthful, and (b) you can’t easily compare the relative strengths of teams. For one thing, the scale isn’t linear. The difference between .98 and .97 is not the same as the difference between .52 and .51 in terms of team strength. Furthermore, what do those numbers mean anyway? They do have meaning – expected winning percentage against an average D-I team – but when comparing two teams it’s not very clear what the difference means.
AdjEM is the difference between a team’s offensive and defensive efficiency. It’s simple subtraction. Even your dog can do it. It represents the number of points the team would be expected to outscore the average D-I team over 100 possessions and it has the advantage of being a linear measure. The difference between +31 and +28 is the same as the difference between +4 and +1. It’s three points per 100 possessions which is much easier to interpret. This measure also makes the SOS and average conference strength numbers less mysterious. (more…)
I’ve produced a few graphs in my day and it’s always comforting when the lines are smooth, like the plot of playing time vs. minutes left in the game: (more…)
We’ve looked at how coaches define foul trouble and got an idea of how each active coach deviates from the norm. Now it’s time to find out how players define trouble.
I’m going forward under the assumption that a player’s effectiveness is more impacted on the defensive end when he has foul trouble. With that as justification, we can look at foul rates to get a hint at when a player feels like he has to play less aggressively on defense. Just like we did when assessing how coaches define foul trouble, I’ll group foul rates by players that have X fouls.
To begin with, let’s look at foul rate given a starter’s foul count in the first half only, adjusted for playing time… (more…)
Let’s take inventory on how every coach in college hoops handles foul trouble. To make this as simple as possible, for each head coach I’m going to determine how often a starter with two fouls is on the floor in the first half. As shown in the previous post in this series, there’s a lot more context to account for with respect to players in second-half foul trouble, so for now, we’ll ignore that in the interest of providing a number that’s simple to interpret.
To make this happen, I’m using the last seven seasons of play-by-play data. I’ve sampled every game at each minute of the first half. For example, with exactly 19:00 left, I’m checking if there is a starter with two fouls and whether that starter is on the floor, then repeating this for 18:00 and each minute thereafter all the way to the end of the half.
This is essentially a random sample of the data, especially when you consider I am throwing out situations where the play-by-play data doesn’t allow one to definitively know who is on the floor. But it’s going to uncover each coach’s tendencies quite well. Just don’t take the percentages beyond the decimal point as a literal record of what’s happened in the last seven seasons. It’s reasonably close to what happened given the limitations of the data. (more…)
In order to have a mature discussion about foul trouble, we need to know what foul trouble is. I could define it myself, but there are really only two groups of people who should be making this determination: coaches and players. These two entities are not completely independent. If a coach treats a player like he is in foul trouble, he’ll probably play like he’s in foul trouble. Nonetheless, as a first step it’s easy to identify what coaches think, so let’s take a look at the data.
What I’ve done here is look at how coaches treat starters. If a starter has X fouls, how likely is he in the game with Y minutes left? Here’s the resulting graph using games since the 2009-2010 season. (more…)
This is the part of year where I have the time to laboriously explore a single topic at my own pace and you have the time to read about it. Welcome to the 2016 Summer Series. This off-season I will be digging into the issue of foul trouble.
Many smart people (mostly non-coaches) believe that benching a star player due to foul trouble is a bad idea. And many smart people (mostly coaches) believe that believe that benching a star player due to foul trouble is a good idea. It is one of the more vexing strategic issues of the sport.
Unlike similar issues in other sports – whether to go for it on 4th down in football, when to pull the goalie in hockey, or just about anything in baseball – there hasn’t been much data-driven work on this issue. The major strategic issues in other sports are able to be analyzed in a way that the motivated human can understand. And those issues have well-accepted solutions that are supported by solid research. (more…)
Congratulations to the winner of 2016 kenpom.com player of the year award, North Carolina’s Brice Johnson. The senior forward finished the season with a 127 offensive rating, which was third in the country among players that used at least 24 percent of their team’s possessions. He got there by making 61 percent of his field goal attempts and 78 percent of his free throws, which produced a true shooting percentage of 64.9, easily a career high and the 25th-best figure in the land. (more…)
FIBA adopted a 30-second shot clock in 1956. Women’s collegiate basketball started using it in 1969. In 2015, men’s college basketball ended decades of trepidation and joined the rest of the world by instituting a 30-second shot clock. Even being nearly four decades behind everyone else, there was still plenty of debate over whether the men’s game was doing the right thing.
In the midst of the one of the lowest scoring seasons since 1952 (exceeded only by 1982 and 2013), 30 percent of coaches polled by Jeff Goodman supported keeping the shot clock at 35. Maybe that figure wasn’t so bad. After all, it’s difficult to get 70 percent agreement on just about any topic. If such polls were conducted before the introduction of the shot clock or 3-point line, I’m guessing we’d find similar levels of dissent. (more…)
Here’s the forecast based on the latest ratings. The biggest winners of the first week were the survivors in the Midwest thanks to the Michigan State upset. The team hurt the most was Oregon, who struggled with Saint Joseph’s and has to deal with a chalk bracket in the West.
Elite8 Final4 Final Champ 1 in Pvs
1MW Virginia 70.8 52.6 33.8 20.8 5 13.2
1E North Carolina 65.8 51.0 29.3 17.1 6 10.3
1S Kansas 72.7 40.9 27.2 15.4 6 15.4
2S Villanova 63.4 34.8 22.7 12.5 8 7.3
2W Oklahoma 56.1 30.5 12.6 5.5 18 5.5
1W Oregon 54.6 27.5 10.8 4.5 22 4.9
5E Indiana 34.2 21.9 9.3 3.9 25 1.6
11MW Gonzaga 58.1 20.0 8.6 3.4 30 0.5
3S Miami FL 36.6 15.3 7.9 3.4 30 1.8
3W Texas A&M 43.9 21.2 7.6 2.9 35 2.4
4MW Iowa St. 29.2 15.9 7.0 2.8 35 1.3
4W Duke 45.4 20.7 7.3 2.7 37 1.2
10MW Syracuse 41.9 11.6 4.1 1.3 75 0.1
5S Maryland 27.3 9.0 3.9 1.3 76 0.9
7E Wisconsin 51.6 14.3 4.3 1.3 76 0.3
6E Notre Dame 48.4 12.8 3.7 1.1 93 0.2
Hey coaches, need a hand with filling out your schedule for the the 2016-17 season? I can’t play matchmaker, but I can provide you a spreadsheet with a first cut of expected ratings for every team next season. These ratings use the same method that has produced the preseason ratings on my site in previous seasons. The projections are mostly based on the quality of returning players and previous team performance, but there is more discussion about the ingredients here.
Data provided will be a list of all 351 D-I teams with overall ranking, and national rankings in offense, defense, and tempo. In addition, for those of you that are into gaming the RPI, teams are ranked by projected conference winning percentage so you can identify opponents which may rack up plenty of wins but be relatively easy to defeat. This data can assist you in finding the type and quality of opponent you are looking for to fill out your schedule for the upcoming season and will not be shared with anyone outside the coaching community until October. Please send a message to ratings at kenpom dot com and we can discuss terms.