Who likes it when players get into foul trouble? Well, maybe nobody, but it’s one of the most interesting strategic aspects of college basketball. While people have been thinking about ways to eliminate the individual limit on personal fouls, I’d feel very strongly about preserving the five-foul limit if officiating were perfect.

Mainly because I don’t like fouls and of all the deterrents to fouling, the personal foul limit is the most important. One can look at how the foul rates of reserves change when they become starters to get an idea of that. Almost surely, any softening of the individual foul limit will result in more fouls being committed. Which leads to more free throws and a slower-paced, less-entertaining game.

Of course, officiating isn’t perfect and never will be, and it sucks when someone gets a foul they don’t deserve. And I’m sure coping with foul trouble is an annoying part of a coach’s job during a game. But it’s also necessary. As I discovered when looking at this issue in 2016, players are more conservative defensively when they get in foul trouble, so expecting coaches to ignore the situation is unrealistic if they are doing their jobs right.

Furthermore, there are reasons you’d rather have a player available for the end of a potentially close game than just simply playing him until he fouls out. On the flip side, a player whose effectiveness is reduced due to foul trouble might still be better than the alternatives and if that player isn’t particularly foul-prone to begin with, there’s little need to bench him. Naturally, each coach handles these decisions differently. (And because this issue doesn’t seem to be well-understood, I suspect many are using a sub-optimal approach.)

With that in mind, I’ve added something called ‘2-Foul Participation’ to the team scouting reports which gives us a blueprint for each coach’s philosophy. Often I find myself watching a game and a player picks up his second foul in the first half. Off to the bench he goes. But is he headed to the bench for good? What are the chances he sees the floor again before intermission? We can’t get inside the coaches head or see the future so there is no way of knowing for sure, but coaches have tendencies. However, unless you watch a lot of games involving the team in question and either have an amazing memory or take a bunch of notes, there is no reference for what a coach’s tendencies are. 

This is an effort to change that. 2-Foul Participation is simply the percentage of time that a starter with two fouls in the first half has been allowed to play. If a starter picks up a foul with ten minutes left in the first half and plays one of those remaining minutes, then he’s participated in 10% of the minutes he could have. Add up the possible minutes for all starters and the minutes on the floor and you get the team’s number for the whole season.

That figure was around 20% for all of big-time college basketball last season, a number that has been dropping steadily since 2010, the first season for which we have complete play-by-play data. Coaches are gradually getting more conservative about playing guys with foul trouble in the first half. Even with foul rates declining to historic lows last season, coaches were less willing to play a guy after he picked up his second foul. It makes one wonder if there is a level of fouling low enough to make coaches reconsider their philosophy.

Perhaps there is some strategic use for this information. I don’t know. If you know a coach will sit a player with two fouls for the rest of the first half, then to some extent that player is already in foul trouble with one foul. They are one foul away from fouling out in the first half! Maybe one can do something with that information or maybe not. This is one of many reasons why I am not a coach.

Mainly, though, this is for informational purposes. You no longer have to guess if a coach will let a player with two fouls play. In addition to the data being added to the team scouting reports, it’s also on the coaching resumes and on a leaderboard page of its own. Now we can be enlightened that Bryce Drew is willing to let his players play while Tony Bennett isn’t.

Why only look at cases of two fouls and not three or four fouls? Well, in an a stunning coincidence, coaches universally agree that starters with two fouls are no longer in trouble when the second half starts. That provides a neat limit on the time frame to examine. There is no similar agreement on when it’s safe to play someone with three or four fouls, requiring the need for a more complex, and thus less transparent, method to measuring foul trouble tendencies in those cases. Besides, I’d expect 2-Foul Participation goes pretty far in explaining how a coach handles those other situations.

As far as the accuracy of this information, the usual caveats apply when deriving stats from play-by-play data. Substitution data can occasionally be wonky, and about 10% of the time it is not possible to know who is on the floor. Nonetheless, given the distinct trends we see among coaches, the data is plenty good enough for our purposes.

There are a few other measures on the leaderboard page. There is the total amount of time starters have two fouls and the total time they are on the floor. This is mainly for bookkeeping purposes and to help you and I catch gross errors in the calculations. I also created an adjusted 2-foul participation measure. In practice, there is a tendency for coaches to give backcourt players more latitude with two fouls and also there are trends in time as well. (Peculiarly, the most likely time for a coach to play someone with two fouls is with 4-5 minutes left in the half.)

Adjusted 2-foul participation accounts for these trends but in reality there are very few coaches for whom this makes a significant difference. I’ve also shown bench minutes on the page. There’s an inverse correlation between the willingness to play guys in foul trouble and how much one trusts one’s bench. Although, the cause and effect aspects of this relationship certainly vary from team to team.

Finally, I’ve provided the distribution of 2FP for all 3,159 teams over the past nine seasons below. The distribution has a long right tail, which means that most teams are below average. For instance, the difference between the 10th and 40th ranked teams last season was the same as the difference between 226th and 351st. So while there’s certainly a fun trivial distinction to being last (congrats, John Beilein for earning the title last season), in reality there’s not much functional distinction between 300th and 351st in any given season.

 

There are a few ways to analyze something but using the wisdom of the crowd is one my favorites. Of course, it depends on the particular crowd. Me, I’d prefer to use the crowd that bets money on things. Anyone can make predictions but history shows the best ones are made by people that put something tangible on the line.

Unfortunately, the types of things I really care about, like who is the best team at any given moment, are not something such money-risking people will tell us directly. Mike Beouy helpfully publishes betting market rankings based on game point-spreads that tries to assess this. But even then, if I want to know the third-best team in the Patriot League according to the crowd, I am out of luck.

Still, there is much to be learned from point spread data. One simple thing to look at is home court advantage. It’s been a personal obsession of mine for a few years now. Over a decade-plus, I have written pieces on HCA ranging from horrible to poor to lame to finally, reasonably useful.1 The slog culminated in my ever-updating home-court ratings for every team in college basketball based on certain game statistics that have been shown to be predictive of future home-court advantage.

Coming up with some sort of regression equation to identify home-court advantage is neat for me but not the most transparent thing from the reader’s perspective. One can fairly easily dismiss the results if they conflict with their own thinking because Ken gave a lot of disclaimers and “regression” might be a code word for “guessing”. The words do sound the same and share a frightening number of letters.

But some skepticism is expected and the conscientious analyst should be poking holes in one’s work, anyway. In that spirit, I was curious to see what the market thought of home-court advantage on a team level. So using all of the point spreads I could get my hands on for the past five seasons, I looked at every case where two teams played in each other’s home venue during the regular season. I took the home-court advantage for each team to be half the difference in the point spread in those two games.

For example on January 8, 2014, Akron was favored by 6.32 points2 at Ball State. In the return game on January 29, the Zips were favored by 13.96. Take the difference of 7.64, cut it in half, and the 3.82 is the assumed home court advantage in the pair of games. (Overly precise, for sure.) That difference is mostly home-court advantage but there were 21 days between games. In that time the market might have taken a different view of one or both teams, or some degenerate with deep pockets could have developed a sudden interest in betting MAC games.

But get enough games for each team and a signal starts to emerge. In the era of unbalanced conference schedules there aren’t that many pairs of games in a given season, and some teams don’t have any games to use because their conferences aren’t worth the time of oddsmakers. But over the past five seasons there were 258 teams that have at least 15 pairs of games to use.

My main interest here is how these numbers compared to my statistical HCA estimates. The team with the largest spread-derived home-court advantage is Hawaii with a value of 4.5. In my system the Rainbow Warriors are ranked 238th with a value of 2.8. That’s not a good start! It turns out that’s the biggest outlier in the comparison, though. Good news for your guy: The market tends to give a larger home-court advantage to the teams that also have a large statistically-derived home court advantage. Here’s a comparison in scatterplot form:

All in all, I’m pretty happy with that. There is some noise for sure, but there will always be. Home court advantage is a moving target, but because there’s no way to precisely measure it, we need years of data using the methods shown here to get a reasonably stable number.

The noise goes both ways, too: There’s noise in my method and noise in the point spread. But given the general agreement in the two methods, there’s additional confidence that we can distinguish between good, average, and bad home courts. As the games threshold is raised to limit the teams included in the market-based method, the correlation between the two methods increases. A games threshold of 40 only leaves me with just 81 teams, but the correlation jumps to 0.72. This suggests that with more data, the two methods would produce converging estimates for each team.3

Circling back, the Hawaii discrepancy is interesting since there isn’t a travel component in my method. Obviously, a 6-hour plane trip should take a lot out of the traveling team. Boston College’s home court is rated poorly by both methods, but it would suddenly be a tough place to win in if they moved to the Pac-12.

It’s also interesting since over the past five seasons, Hawaii’s home/road difference in scoring margin in conference games has been a mere 4.13 points, suggesting a home-court advantage of around 2. The team has gone 25-15 at home while going 21-19 on the road. According to the market, Hawaii has been either underachieving at home or overachieving on the road for an extended period of time. And based on its performance against the spread, it is entirely the former.

As I said, I have a lot of confidence in the crowd, so I fully expect Hawaii does have a strong home court. But if we can’t see that in the results over a five-year period, it should give you some appreciation for (a) how difficult it is to measure home-court on a team level and (b) how little difference there is between the best and worst home-courts in the country.

Teams that run up a long home-court winning streak are doing it mostly because they are better than their opponents and they just happened to save their worst performances for either poor opponents at home or games on the road. Sure, the home court contributes to a long winning streak, too, but any special advantage a team has in their building is a very small factor.

One other interesting thing to look at is the season average for home-court. It’s been documented that home-site advantage is decreasing across different sports and leagues in recent years and college basketball is no different. The market has followed this trend, except for last season.

Season  HCA   Games
 2014   3.68   1626
 2015   3.46   1730
 2016   3.35   1858
 2017   3.23   1824
 2018   3.30   1668

The odd thing is that in real life, home-court advantage did make a resurgence last season, with conference winning percentage increasing to 61.0% for home teams after reaching a historic low of 59.0% in 2017. It’s an unexpected change especially if one believes fouls are an important player in home-court advantage, since fouls called per possession plummeted to a level not seen in at least two decades.

Next, let’s take a look at the average HCA by conference.

 rk Conf  HCA
  1 B12   4.0  
  2 SEC   3.8  
  3 B10   3.8  
  4 MWC   3.7  
  5 P12   3.7  
  6 Sum   3.7  
  7 BE    3.5  
  8 BSky  3.5  
  9 BW    3.4  
 10 MVC   3.4  
 11 ACC   3.4  
 12 MAC   3.4  
 13 SB    3.4  
 14 A10   3.4  
 15 Amer  3.3  
 16 WCC   3.2  
 17 CUSA  3.2  
 18 CAA   3.2  
 19 Horz  3.2  
 20 SC    3.1  
 21 MAAC  3.0  
 22 OVC   2.9  
 23 Ivy   2.9  

And finally, the market-based home-court values for each team with at least 15 game pairs to use.

                                  H/A
 rk Team                   HCA   Pairs
  1 Hawaii                 4.5     26
  2 Denver                 4.4     37
  3 LSU                    4.4     24
  4 West Virginia          4.3     42
  5 Arkansas               4.2     23
  6 Missouri               4.2     25
  7 Iowa                   4.2     27
  8 Alabama                4.2     23
  9 Baylor                 4.2     43
 10 Oklahoma               4.1     45
 11 Washington             4.1     35
 12 BYU                    4.0     45
 13 New Mexico             4.0     37
 14 Kansas                 4.0     44
 15 Utah                   4.0     35
 16 Kansas St.             4.0     43
 17 Iowa St.               4.0     41
 18 Texas                  4.0     41
 19 Indiana                4.0     27
 20 Boise St.              4.0     38
 21 Old Dominion           4.0     20
 22 Nebraska Omaha         4.0     37
 23 Michigan               3.9     26
 24 Washington St.         3.9     34
 25 Texas Tech             3.9     43
 26 Fresno St.             3.9     37
 27 Oklahoma St.           3.9     44
 28 Arizona                3.9     34
 29 Texas A&M              3.9     24
 30 Utah St.               3.9     36
 31 Colorado               3.9     34
 32 Georgia St.            3.9     40
 33 Mississippi St.        3.9     24
 34 Montana St.            3.8     36
 35 Buffalo                3.8     33
 36 Ohio St.               3.8     26
 37 Georgia                3.8     25
 38 Marshall               3.8     21
 39 Penn St.               3.8     25
 40 North Carolina St.     3.8     20
 41 Purdue                 3.8     26
 42 Oral Roberts           3.8     30
 43 Nebraska               3.8     27
 44 South Carolina         3.8     24
 45 Maryland               3.8     23
 46 Air Force              3.7     38
 47 Florida St.            3.7     20
 48 Oregon St.             3.7     34
 49 Rutgers                3.7     28
 50 Wyoming                3.7     38
 51 Notre Dame             3.7     18
 52 San Diego St.          3.7     37
 53 Mississippi            3.7     25
 54 Oregon                 3.7     34
 55 Illinois               3.7     27
 56 Minnesota              3.7     27
 57 UNLV                   3.7     38
 58 Arizona St.            3.7     34
 59 Duke                   3.7     20
 60 Xavier                 3.7     44
 61 TCU                    3.7     44
 62 Sacramento St.         3.7     37
 63 Providence             3.7     44
 64 Massachusetts          3.6     22
 65 Tennessee              3.6     25
 66 Southern Utah          3.6     37
 67 South Dakota St.       3.6     37
 68 Colorado St.           3.6     37
 69 Arkansas St.           3.6     42
 70 SMU                    3.6     39
 71 Seton Hall             3.6     43
 72 Marquette              3.6     43
 73 Nevada                 3.6     38
 74 Montana                3.6     38
 75 Creighton              3.6     44
 76 Florida                3.6     24
 77 Eastern Michigan       3.6     35
 78 Davidson               3.6     24
 79 Louisville             3.6     23
 80 North Dakota St.       3.6     37
 81 Georgia Tech           3.6     19
 82 Georgetown             3.6     43
 83 Northern Arizona       3.6     37
 84 Cal St. Northridge     3.6     31
 85 Butler                 3.6     44
 86 Portland St.           3.5     37
 87 Ball St.               3.5     35
 88 Louisiana Lafayette    3.5     42
 89 Weber St.              3.5     36
 90 Memphis                3.5     38
 91 Michigan St.           3.5     27
 92 UCLA                   3.5     34
 93 Missouri St.           3.5     40
 94 Kentucky               3.5     24
 95 Valparaiso             3.5     41
 96 Indiana St.            3.5     41
 97 Wake Forest            3.5     20
 98 Portland               3.5     43
 99 Louisiana Tech         3.5     21
100 VCU                    3.5     22
101 Illinois St.           3.5     43
102 Toledo                 3.5     35
103 Loyola Chicago         3.5     42
104 Central Michigan       3.5     33
105 California             3.5     34
106 Cincinnati             3.5     39
107 Northwestern           3.5     27
108 South Dakota           3.5     36
109 Miami FL               3.5     19
110 Northern Colorado      3.5     35
111 Richmond               3.5     23
112 George Washington      3.5     22
113 Wisconsin              3.5     26
114 Georgia Southern       3.5     37
115 Auburn                 3.5     23
116 IUPUI                  3.5     39
117 UT Arlington           3.5     41
118 Vanderbilt             3.4     25
119 Western Kentucky       3.4     28
120 William & Mary         3.4     43
121 Bradley                3.4     41
122 Saint Louis            3.4     22
123 UC Davis               3.4     31
124 Charlotte              3.4     19
125 St. John's             3.4     44
126 Ohio                   3.4     35
127 Southern Illinois      3.4     41
128 Chattanooga            3.4     40
129 The Citadel            3.4     39
130 Stanford               3.4     34
131 UCF                    3.4     38
132 Duquesne               3.4     21
133 Pittsburgh             3.4     19
134 Eastern Washington     3.4     36
135 East Tennessee St.     3.4     34
136 Dayton                 3.4     24
137 UNC Wilmington         3.4     43
138 UC Riverside           3.4     32
139 Connecticut            3.4     39
140 Little Rock            3.4     39
141 Fort Wayne             3.4     36
142 Miami OH               3.4     34
143 Cal Poly               3.4     32
144 North Carolina         3.3     20
145 Idaho St.              3.3     36
146 La Salle               3.3     23
147 Gonzaga                3.3     42
148 Evansville             3.3     42
149 Akron                  3.3     35
150 East Carolina          3.3     29
151 North Dakota           3.3     36
152 Towson                 3.3     43
153 San Diego              3.3     45
154 Fordham                3.3     22
155 Pacific                3.3     44
156 Hofstra                3.3     42
157 Virginia               3.3     19
158 UNC Greensboro         3.3     40
159 Wichita St.            3.3     41
160 Cleveland St.          3.3     42
161 Kent St.               3.3     35
162 Saint Joseph's         3.3     23
163 USC                    3.3     34
164 South Florida          3.3     38
165 Troy                   3.3     41
166 Northern Kentucky      3.2     27
167 Drake                  3.2     43
168 Elon                   3.2     40
169 James Madison          3.2     42
170 Green Bay              3.2     41
171 Detroit                3.2     40
172 Villanova              3.2     44
173 Louisiana Monroe       3.2     41
174 Rice                   3.2     20
175 Western Illinois       3.2     36
176 Southern Miss          3.2     21
177 Houston                3.2     37
178 Northern Iowa          3.2     43
179 Northeastern           3.2     43
180 Western Michigan       3.2     33
181 Appalachian St.        3.2     38
182 Wright St.             3.2     42
183 Texas St.              3.2     41
184 Siena                  3.2     42
185 San Jose St.           3.2     36
186 UC Santa Barbara       3.1     31
187 College of Charleston  3.1     42
188 UTSA                   3.1     20
189 Eastern Illinois       3.1     27
190 Rhode Island           3.1     23
191 Pepperdine             3.1     44
192 Samford                3.1     38
193 North Texas            3.1     20
194 Temple                 3.1     38
195 Middle Tennessee       3.1     21
196 Bowling Green          3.1     34
197 George Mason           3.1     23
198 South Alabama          3.1     40
199 Oakland                3.1     42
200 Austin Peay            3.1     26
201 Boston College         3.1     18
202 Clemson                3.1     20
203 Northern Illinois      3.1     33
204 Wofford                3.1     40
205 Milwaukee              3.1     41
206 Morehead St.           3.1     26
207 Long Beach St.         3.1     31
208 Niagara                3.1     43
209 Canisius               3.1     45
210 Cal St. Fullerton      3.0     32
211 Brown                  3.0     35
212 Tennessee Tech         3.0     26
213 Iona                   3.0     41
214 St. Bonaventure        3.0     22
215 Furman                 3.0     39
216 DePaul                 3.0     45
217 Eastern Kentucky       3.0     26
218 UAB                    3.0     21
219 Marist                 3.0     44
220 Santa Clara            3.0     44
221 Quinnipiac             3.0     42
222 Tulane                 3.0     30
223 Harvard                3.0     35
224 Drexel                 3.0     43
225 Mercer                 3.0     35
226 Tennessee Martin       3.0     26
227 Saint Mary's           3.0     44
228 Dartmouth              3.0     35
229 Belmont                3.0     27
230 Idaho                  3.0     27
231 Youngstown St.         3.0     42
232 Illinois Chicago       3.0     42
233 Virginia Tech          2.9     19
234 Fairfield              2.9     44
235 Syracuse               2.9     20
236 Tennessee St.          2.9     26
237 FIU                    2.9     21
238 Western Carolina       2.9     38
239 UTEP                   2.9     21
240 VMI                    2.9     34
241 Monmouth               2.9     46
242 San Francisco          2.9     43
243 Manhattan              2.9     45
244 Rider                  2.9     45
245 Southeast Missouri St. 2.9     26
246 Yale                   2.9     34
247 Saint Peter's          2.9     47
248 Delaware               2.9     41
249 Loyola Marymount       2.8     44
250 Princeton              2.8     35
251 Tulsa                  2.8     31
252 Penn                   2.8     35
253 Columbia               2.8     35
254 Murray St.             2.8     26
255 Cornell                2.7     34
256 SIU Edwardsville       2.7     27
257 Jacksonville St.       2.7     27
258 Florida Atlantic       2.5     21

   [ + ]

1. But even the last piece got rejected by three different outlets before I decided to post it on my blog.
2. It’s 6.32 because I’ve averaged a bunch of different sports books. All data is taken from donbest.com.
3. Further illustrating this point, on a conference-level, the correlation is 0.78.

Hey coaches! Need a hand filling out your schedule for the the 2018-19 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, incoming transfers 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 want 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 for more details and pricing.

                    Elite8 Final4 Final Champ
 1E  Villanova       72.5   48.5   31.1  23.3
 2MW Duke            81.1   54.0   28.8  20.4
 4W  Gonzaga         68.9   40.0   25.9  10.7
 2E  Purdue          61.1   26.3   13.7   8.7
 3W  Michigan        62.3   32.7   20.0   7.8
 5S  Kentucky        63.6   37.7   17.8   5.8
 1MW Kansas          53.9   22.2    8.5   4.8
 7S  Nevada          56.9   27.6   11.4   3.2
 5MW Clemson         46.1   17.5    6.1   3.2
 5E  West Virginia   27.5   12.4    5.1   2.8
 3E  Texas Tech      38.9   12.8    5.2   2.8
 7W  Texas A&M       37.7   15.1    7.3   1.9
11S  Loyola Chicago  43.1   18.2    6.4   1.5
 9W  Florida St.     31.1   12.2    5.6   1.3
 9S  Kansas St.      36.4   16.4    5.6   1.3
11MW Syracuse        18.9    6.3    1.4   0.5
                             Rd2   Swt16  Elite8 Final4 Final  Champ
 1E  Villanova               97.4   81.9   62.5   43.7   28.7   18.1   
 1S  Virginia                96.7   78.1   59.9   41.4   28.9   17.6   
 2MW Duke                    95.9   80.3   51.8   36.9   20.7   12.1   
 2S  Cincinnati              87.3   62.8   42.1   21.8   13.1    6.6   
 2E  Purdue                  94.3   68.7   45.8   22.7   12.4    6.6   
 2W  North Carolina          95.9   70.9   43.6   25.8   12.4    5.7   
 3MW Michigan St.            89.4   63.8   31.9   20.5   10.1    5.2   
 4W  Gonzaga                 82.6   53.1   33.4   18.6    8.9    4.0   
 1MW Kansas                  88.8   59.4   35.5   15.2    6.5    2.9   
 3W  Michigan                76.5   46.8   24.5   13.1    5.8    2.4   
 1W  Xavier                  97.9   63.5   31.3   15.2    6.2    2.3   
 3S  Tennessee               88.1   57.5   27.6   11.5    5.5    2.2   
 3E  Texas Tech              85.4   53.3   24.7    9.5    4.0    1.8   
 5W  Ohio St.                74.4   35.5   19.2    9.2    3.6    1.4   
 4MW Auburn                  84.1   47.6   24.4    8.8    3.1    1.2   
 5E  West Virginia           71.4   42.6   14.4    6.8    2.9    1.2   
 6W  Houston                 65.3   33.0   15.0    7.2    2.7    1.0   
 5MW Clemson                 65.7   35.6   17.8    6.5    2.4    0.9   
 5S  Kentucky                62.4   36.7   12.5    5.7    2.5    0.9   
 4E  Wichita St.             84.1   43.6   13.7    5.9    2.3    0.9   
 4S  Arizona                 71.8   37.5   12.2    5.3    2.2    0.8   
 6E  Florida                 63.8   30.5   11.8    3.7    1.3    0.4   
 7S  Nevada                  56.5   20.1    9.7    3.2    1.2    0.4   
 6MW TCU                     61.6   22.8    7.6    3.5    1.1    0.4   
10E  Butler                  55.8   18.2    8.6    2.6    0.9    0.3   
 8MW Seton Hall              55.5   22.5   10.4    3.1    0.9    0.3   
 7W  Texas A&M               61.9   19.9    8.4    3.2    1.0    0.3   
 8S  Creighton               56.0   12.4    5.8    2.2    0.8    0.2   
 6S  Miami FL                51.5   20.8    7.1    2.1    0.7    0.2   
 8E  Virginia Tech           57.1   11.1    4.7    1.7    0.5    0.2   
 8W  Missouri                49.1   17.9    6.5    2.2    0.6    0.2   
11S  Loyola Chicago          48.5   19.0    6.5    1.9    0.6    0.2   
 9W  Florida St.             50.9   18.4    6.5    2.2    0.7    0.2   
10S  Texas                   43.5   13.6    5.8    1.7    0.6    0.1   
12S  Davidson                37.6   17.1    4.3    1.5    0.5    0.1   
 7E  Arkansas                44.2   12.1    5.0    1.3    0.4    0.1   
 9S  Kansas St.              44.0    9.1    3.8    1.3    0.4    0.1   
 9MW North Carolina St.      44.5   15.8    6.2    1.5    0.3    0.09  
 7MW Rhode Island            49.8    9.9    3.0    1.1    0.3    0.07  
11W  San Diego St.           34.7   12.6    3.8    1.3    0.3    0.06  
12MW New Mexico St.          34.3   13.1    4.5    1.0    0.2    0.05  
10MW Oklahoma                50.2    9.0    2.6    0.9    0.2    0.05  
 9E  Alabama                 42.9    6.5    2.3    0.7    0.2    0.04  
11E  UCLA                    22.9    8.6    2.5    0.6    0.1    0.04  
11MW Arizona St.             20.5    5.7    1.4    0.5    0.1    0.04  
12E  Murray St.              28.6   11.0    2.1    0.6    0.1    0.03  
11MW Syracuse                17.9    5.1    1.2    0.4    0.09   0.02  
10W  Providence              38.1    8.6    2.6    0.8    0.2    0.02  
14W  Montana                 23.5    7.6    1.9    0.5    0.1    0.02  
13S  Buffalo                 28.2    8.7    1.4    0.3    0.07   0.01  
12W  South Dakota St.        25.6    6.3    1.8    0.4    0.08   0.007 
13W  UNC Greensboro          17.4    5.1    1.4    0.3    0.06   0.007 
11E  St. Bonaventure         13.4    4.1    1.0    0.2    0.03   0.004 
15S  Georgia St.             12.7    3.5    0.9    0.1    0.02   0.001 
14MW Bucknell                10.6    2.6    0.3    0.07   0.009  0.001 
14S  Wright St.              11.9    2.8    0.3    0.04   0.006  0.001 
13E  Marshall                15.9    2.8    0.2    0.02   0.004  0.001 
13MW College of Charleston   15.9    3.7    0.7    0.08   0.01   <.001 
14E  Stephen F. Austin       14.6    3.5    0.5    0.05   0.008  <.001 
16MW Penn                    11.2    2.3    0.4    0.03   0.006  <.001 
15MW Iona                     4.1    0.9    0.09   0.02   0.003  <.001 
15E  Cal St. Fullerton        5.7    1.0    0.1    0.009  <.001  <.001 
15W  Lipscomb                 4.1    0.7    0.07   0.006  <.001  <.001 
16S  UMBC                     3.3    0.4    0.04   0.006  <.001  <.001 
16E  Radford                  2.4    0.5    0.06   0.005  <.001  <.001 
16W  Texas Southern           1.5    0.09   0.004  <.001  <.001  <.001 
16W  North Carolina Central   0.6    0.04   0.002  <.001  <.001  <.001 
16E  LIU Brooklyn             0.2    0.01   <.001  <.001  <.001  <.001 

If you are looking my annual conference tournament probabilities, they are being posted to my twitter account.

Hey everyone. Now that the beginning-of-the-season rush has settled down, it’s a good time to go over some of the new features on the site.

The short version is:

– New and improved win probability graphs (with FanMatch updates)
– Team records
– Age and position information on the player pages (when available)
– Better position information

Now, the long version:

Win probability graphs

The biggest project of the offseason was upgrading the win probability charts. I hadn’t really touched these since their initial unveiling in the offseason of 2010. For one thing, they look more professional now, almost like you could put them on a real web site.

I also refreshed the algorithm used to calculate the win probabilities which was last updated in 2012. The method is the similar as back then, but I am making use of the results of the tens of thousands of games played since then. One change in methodology is that the initial state is determined by the algorithm itself and not by the percentages given by the ratings. Often, these differences are small. 

In addition, each win probability chart contains additional measures to describe the game. These are comeback, excitement, tension, and dominance. The charts now show the ranking in each category among all games played in its respective season. I will briefly explain each of these.

Comeback measures the minimum win probability for the winning team. Please remember that the win probability accounts for the relative strength of the competing teams. Therefore, a team can overcome a low win probability without necessarily overcoming a large deficit on the scoreboard.

Excitement measures the length of the win probability graph assuming both teams are equal. (This is not the same as the plot you see on the graph which accounts for the relative strengths of the teams.) The most exciting game of the season as of this writing was Friday night’s Florida/Gonzaga game. That game was pretty exciting and I think would win a vote of sportswriters so far this season.

However, the excitement index doesn’t care about the quality of the teams involved. By the end of the season, the most exciting game is as likely to be from the SWAC as it is the SEC.

Excitement is scaled for overtime, so it is measuring something like excitement per minute. Although the most exciting things happen when the game is on the line, so overtime games will usually be quite exciting by this measure. But some overtime games are not that exciting until very late in regulation.

The value listed for excitement is not terribly meaningful. I like to think that a value of one or more means you got your money’s worth and a negative value means you should demand a refund. The entity which sold you the ticket may or may not agree.

Excitement will also have a bias towards high-possession/high-scoring games. For fans of low-possession/low-scoring games, there’s the tension measure. 

Tension measures the area between the win probability plot (assuming equal teams, so not the one shown) and the 50% line. The most tense game to date was an overtime affair between Sacred Heart and Maine on November 19. Neither team led by more than six over the entire 45 minutes.

Dominance is nearly the inverse of tension. Behind the scenes, I have a model that predicts the final margin at any point in the game assuming the teams are equal. Dominance is measured using the average predicted margin during the game for the winning team. It is not terribly useful, but I needed another score for some symmetry. Build a big lead early and continue to run up the score during the game, win by like 60, and you’ll post a high dominance score. The most dominant game so far was last Thursday’s late-late-late night game between San Diego State and Sacramento State. The Aztecs built a 49-14 halftime lead en route to an 89-52 victory. Go team.

These measures are not meant to settle arguments nor designed to be publication-worthy algorithms. They are simply useful guides to get a feel for how a game was played. If you feel that you witnessed a game more exciting than Florida vs. Gonzaga this season, you probably did. Congrats!

The excitement and comeback measures are also on the FanMatch™ page now with top 100 season ranks noted where appropriate. These are mainly for reference. If you couldn’t stay up to see the Florida/Gonzaga game (no shame in that because it ended at about 2:30 AM ET) then you could have reviewed the FanMatch™ page the next morning and noticed that you missed a crazy game. You can check out the win probability and box score to get an idea of what happened. Then consult the work of local beat writers for quotes and such. You will never miss anything important in college hoops again!1

Team records page

There is a team records page available from the team history page. It lists various game records for each team over the history of the site. Wondering where your team’s recent game ranked all-time in turnover percentage since the 2002 season? Well, if it’s in the top ten best or worst, that page will tell you the answer.

Player page enhancements

Player birthdate and age is now listed on the player page when that information is known. Shown is the player’s age as of January 1st of the appropriate season along with their age relative to their class average. In addition a player’s position for each season is shown provided the player played at least 10% of his team’s available minutes and the season is in the play-by-play era which begins with the 2010 season.

Position identification improvements

The position identification for the depth chart has been improved by leveraging play-by-play data to better identify the point guard that is on the floor. I’m sure you can find teams where the algorithm mis-identifies the point guard, but it is slightly more difficult than before. This has been applied to previous seasons and after a few more games, the 2018 season depth charts will take advantage of play-by-play stats as well.

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1. Assuming timely dissemination of play-by-play data by the home team.

App news

Some of you who have used the kenpom app lately may have noticed that you are unable to log in. (You may be lucky for a while if you have an Android.) Without getting into the technical causes of this, it’s the symptom of a larger issue.

The talented people that originally designed the apps have moved on to better things and as the features on the site have grown, the functionality of app has lagged behind. Given the constant evolution of the site, it makes more sense to make the site itself more mobile-friendly. Going forward this is a better solution, allowing mobile users to more easily access all the features available on the site, especially as new ones are added. Therefore, the apps will no longer be supported.

This will likely not be a fast process but you should see improvements gradually rolled out during the season. Thanks for your understanding and patience.

In our last installment, we discussed the degree to which past home-scoring advantage predicts future home-scoring advantage. We can look at other stats and derive home advantages from them. For an example, let’s use home steals advantage. Like home-scoring advantage, this is the difference between a team’s home steal margin and road margin.

In conference games from 2002-2009, Alabama averaged 1.32 more steals than their opponents at home and averaged 1.98 fewer steals than their opponents on the road. That gives a home-steals advantage of 1.66, which led all teams during that time.

Does home-steals advantage predict future home-scoring advantage? Well, no, not at all. And most box-score stats don’t. But there is one stat that actually outperforms points. If we just use home-foul advantage by itself to predict future “home-court advantage”, it does significantly better than home-scoring advantage. Here are the predictions compared to the observed values based on using previous home-scoring advantage (HCA) and previous home-foul advantage (HFA).

It’s not a huge difference, but our cloud of data points is getting stretched out along the line of perfect predictions a little bit. Of course, using both home-foul advantage and home-scoring advantage is even better to predict future home-court advantage. The plot on the right below illustrates the predictions of future home-scoring advantage made from such a model.

So it’s pretty clear that fouls drive home-court advantage more than any other thing recorded in the box score.1 Another piece of evidence that foul bias is an important source of home-court advantage is the identical trend in home-court advantage and home-foul advantage2 over the past decade-and-a-half. 

As far as what’s causing the decrease in home-foul advantage, one can only speculate. As officials and their supervisors get more and quicker access to video from games, it stands to reason that they would become more fair about making calls. But it’s also possible that less enthusiastic fan support may be decreasing the home crowd’s influence on officials.3 Or players themselves may be more prepared in road games than they used to be for some reason. Whatever the source, the result appears to be a fairer whistle towards the road team.

The next step is to make predictions for the home-court advantage of each team.  So far, I’ve been using eight years of training data and eight years of target data. It obviously helps to have more seasons to stabilize some of the data, but just using half of our 16-year sample to predict the other half limits us to 325 data points.4 We could use the previous two years to predict the next season of home-court advantage. This has the drawback of increasing the error of predictions, but it has the benefit of increasing our sample by a factor of 15.

After considerable experimentation, I’m using the past six seasons of data for each team to predict its next two seasons of home-court advantage. This gives the value for each some stability while allowing flexibility for home-court numbers to change over time.

The increase in samples for the model revealed two other stats of importance: non-steal turnovers and blocks. Home advantages in both of those categories influence the model as well. And finally, a team’s elevation is included in the model.

I must warn you that any predictions of team-specific home-court advantage will be noisy. I mean, just look at the plot of predicted vs. actual HCA based on home-foul and home-scoring advantage. A lot of those points stray pretty far from the line of perfect predictions. And those are in-sample predictions. Any predictions for the next few years will undoubtedly be more noisy than that.

The result is that determining which team has the best home-court advantage is impossible with any degree of certainty. Even distinguishing between the tenth-best and 60th-best home-court advantage is on shaky ground. It’s probably best to think of of home-court rankings in groups of three. Teams rated in the top-third probably have an above-average home-court advantage and teams in bottom-third probably have a below average home-court advantage.

Nonetheless, I have posted a team’s home-court advantage computed from this method at the bottom of each team’s schedule. In light of the previous paragraph, the rankings and values are mostly for conversational fodder at your next cocktail party.

Along those lines, the team that currently owns the top ranking in home-court advantage is Air Force, at 4.5 points. An unlikely choice to be sure, and it’s worth repeating that we can never know which team has the best home-court advantage. It is probably not Air Force, a team that plays in a dingy 5,800 seat arena that requires clearing a security checkpoint to get to.

However, it’s illustrative of the fact that home-court advantage and team quality do not have to be related. The Falcons own the second-longest active losing streak in road conference games at 22. And over that time they’ve gone 13-10 in conference games at home. It’s a safe bet they benefit from an above-average home-court advantage.

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1. This is largely consistent with findings offered by Tobias Moskowitz and Jon Wertheim in the book Scorecasting: The Hidden Influences Behind How Sports Are Played and Games Are Won.
2. It’s important to note that a team’s home-foul advantage is not simply how many fewer fouls that team commits at home compared to its opponents. It’s the team’s foul difference at home relative to its foul difference on the road.
3. There’s another question about what drives home-foul bias and given that teams from better conferences tend to benefit from home-foul advantage the most, one could infer that the crowd has a significant influence on this.
4. Additionally, a team’s home-court advantage is probably not constant over an eight-year period.

Who has the largest home-court advantage in college basketball? How many points is it worth? Well, I have some bad news for you: It is really impossible to answer these questions with much precision. We can look at the box score of a game and see how many points a team won by or how many points each player scored.

Maybe one could do a little more fancy maths and make an estimate on what each player contributed to the point total. But we can’t tell how many points the home court contributed. Maybe someday, but for the most part people don’t care as much about the value of home-court as they do about the value of a star player. So that’s where the research focus lies.

But it’s a fun analytic exercise to try determine the value of home-court. I’ve been thinking about it for a while, starting a few years ago I wrote this article about home-court advantage at ESPN. The analysis was rather primitive and I apologize to everyone for paying their hard-earned Insider money for that. Basically, it identified the teams that had the largest difference between their home and road scoring-margin in conference play. Those teams probably had the largest home-court advantage, right?

For instance, from 2002-2009, Texas Tech outscored its opponents by an average of 6.0 points in conference games in Lubbock and were outscored by 9.3 points per conference game on the road. The difference of 15.3 suggests a home-court advantage of 7.6 points per game relative to a neutral court. That figure was the highest among 320 teams that played at least 50 conference games over that time. (more…)