## Saturday, December 9, 2017

### Points Per Minute Deficit (NCAAB)

As any diehard fan can tell you, when your team is down, you won't count them out until a comeback just isn't feasible anymore, models and simulations be damned. But there has to be some degree of reasonableness, right?

Growing up a Carolina basketball fan, I always had certain heuristics in the back of my mind (instilled by my dad) on whether we could come back or not. The thinking went that if we could keep the score within the number of minutes left in the game, then we had a chance to chip away until we tied or took the lead at a rate of 1 point per minute. For example, if UNC trailed by 10 points with 9:50 left to play, then we needed to get to within 9 by 9:00 left to play. And then trail by 8 (or better) by 8:00 left, 7 by 7:00 left, etc. Note that this doesn't mean that the trailing team went on to win necessarily; all it means is that the trailing team either tied or took the lead at some point later in the game.

I aimed to determine if this heuristic had any basis in real-life results by looking at play-by-play NCAAB data for the entire 2016-17 season. I scraped KenPom's play-by-play win probability graphs to get scoring data on every possession, which ultimately gave me 393,719 individual possessions in 2nd half/overtime to analyze. Of those, 13,917 (3.5%) matched the criteria of being down x points with x minutes remaining.

There actually is a fairly linear relationship for this idea, with a largest deficit implying a lesser chance of a comeback (as you would expect):

Using linear regression, the relationship as shown above can be described as:

%_Comeback = -0.021*Min_Remaining + 0.409

This model has a fairly good fit too, with an r-squared value of 0.828.

For my purposes, I usually applied this thought process up until UNC trailed by 6 or less (a 2 possession game). At that point all you need is to make 2 threes and it's tied. However, based on the above graph it appears I should ONLY hold out hope when the deficit is within 2 possessions or less, since that is the only stretch in which the trailing team has better than a 1 in 4 chance of coming back. Good luck telling that to any fan of a team down by 7 or more though.

## Friday, November 17, 2017

### Free Throw % Splits Based on Shot Type

I've always been a solid free throw shooter: I shot 75% over 4,250 attempts during the insane three-year stretch where I tracked every single shot I took on the basketball court (and 85% over 630 attempts in 2016)). But when I played organized basketball when I was younger I would always brick free throws when I shot technical foul shots and was alone at the line (although never as badly as my intramural teammate who had an 0 for 34 stretch shooting free throws). Was this just an abnormal anecdotal observation, or does NBA play-by-play data back it up?

It's been previously established that free throw shooters improve from their first shot to their second/third. Nylon Calculus has a great database on this phenomenon from 1999-2016, and TrueHoop suggested in 2011 that the first attempt is like getting to practice free throws in the middle of the game. But what about other splits like when you're alone at the line, or when you can take only one shot (such as an and-1)?

I pulled down NBA play-by-play data for the full 2016-17 season from BigDataBall (a great source at a relatively cheap price for all play-by-play data back to 2006) and looked at every shot attempt and found the same thing previous studies had:

Free throw shooters get better on their second/third attempts, to a very high degree of statistical significance. Comparing the shooting percentage between the first vs second/third shot gives a z-score of 13.56, which has an associated p-value of 1. Even comparing first vs second attempts gives a z-score of 12.54, which has an associated p-value of 1 as well.

But what about my earlier considerations regarding being alone at the line (with no rebounders around you)? I looked at "normal" free throws (on an and-1, two-point shot, or three-point shot) compared with technical/flagrant/clear path foul shots where the shooter is "alone":

3% of all free throw attempts last season occurred where the shooter was "alone", and players shot significantly better in this case, going against my hypothesis. But these "alone" attempts include technical foul shots, which are taken by the best free throw shooter on the floor (the shooting team gets to choose who shoots them). If I remove technical foul shots I get a different picture:

picture that illustrates no significant difference in make percentage (z-score of 0.35 with an associated p-value of 0.64, which is inconclusive).

My final look focused on only the first free throw taken in a set: does the shooter perform better or worse if they know they're getting additional attempts? I.E. Is an and-1 different from the first shot from a set of 2 or 3 attempts?

As before, I filtered out technical shots, and as before, there's no significant difference (z-score of 0.29, p-value of 0.61).

All in all it seems my experience was abnormal: NBA free throw shooters do improve after their first attempt, but make their shots regardless of whether there are other players around them. This makes sense, since they are professional athletes and I am not.

## Sunday, November 12, 2017

### Whether Punt Returners Should Return Punts Inside Their Own 10 Yard Line (NCAAF)

A couple of years ago I wrote this post assessing whether a kick returner should return the kickoff out of the end zone, stemming from my frustration when a college kick returner chooses to forgo the free 25 yards and tries to be a hero and run the kick all the way back. I concluded that the risk/reward balance was actually fairly even when accounting for turnovers. So I now have a new source of frustration to investigate from a game theory perspective: whether punt returners should return punts from inside their own 10 yard line.

I've always thought that a standard unwritten rule for punt returners is to plant yourself at the 10 yard line and let anything kicked over your head go into the end zone. Instead, I've observed many a punt returner attempt to return it from inside their own 10 (UNC's own Ryan Switzer would notoriously make me irate doing this). Are they making a negative risk/reward decision or am I wrong in my steadfast belief that it's a bad decision?

I gathered three full seasons of play-by-play data, using 2011-2013 (since that's what was readily available from this great Reddit thread for NCAAF data). There were almost 15,000 punt returns over this stretch, of which 1,387 were inside the 10 (9.3%). Whittling it further, 669 were actually fair caught (48%), leaving me with 718 punt returns that were caught inside the 10 yard line and returned.

The average punt meeting this criteria was caught at the 7.36 yard line and was returned for 8.98 yards, thus bringing the ball out to the 16.34 yard line. 76% of the time the returner gained yardage, while 12% of the time the returner lost yardage (the other 12% resulted in no gain).

Of the 718 returns, 10 times the punt was ran back for a touchdown (1.4%). On the flip side, 30 returners fumbled and lost the fumble (4.2%), and an additional 2 returns resulted in a safety (0.3%). On its face, it appears my intuition is correct: it's way more risky to try to run it back. But which option is optimal? Return it, fair catch it, or let it bounce towards the end zone?

## Sunday, October 22, 2017

### Simulated World Series Preview 2017: LAD vs HOU

Over the course of my modeling career, I'm so far 0-2 when picking the World Series winners (I had KCR in 2014 and NYM in 2015). That's baseball for you, where the "favorite" only has about a 55% chance of winning a given 7 game series. This year's matchup features a matchup of two 100-win teams, the Dodgers and the Astros.

I've tweaked my simulator that I used in 2015 and simulated the World Series 10,000 times, and predict the Dodgers win it all in 6 games. So if you want to read too much into an extremely small sample size of 2014 and 2015, that means the Astros are winning it in 5 or 7.

My ratings are very in line with Baseball Prospectus, and have the two teams as fairly evenly matched:

HOU: 0.592

So this series should go 6 or 7 games:

The pick: Los Angeles Dodgers in 6

## Saturday, July 22, 2017

### Preseason NCAAF Rankings for 2017

As I did last year, the year before, and the year before that, I've created a new set of preseason NCAAF rankings that take into account player turnover and recruiting classes. The following is literally copied and pasted from last year's write-up so you don't have to click that first link:

As before, I used my final Composite ratings from the MDS Model (from last season) as the base (which takes into account both a forward-looking predictive component and a past-performance only retrodictive component), and then factored in ESPN's Preseason FPI and the S&P+ projections, both of which take into account player changes on each team. Once the season starts, this "preseason" rating will be faded out as the season progresses, carrying less and less weight with each ensuing week.

In the below list, the "Trend" indicates whether the respective team's new ranking rose or fell relative to the average of last year's end-of-season Composite ratings. We have two new team's this year: Coastal Carolina (moving up from FCS) and UAB (welcome back!).

 Rank Team PRESEASON Trend 1 Alabama 0.924 UP 2 Ohio State 0.863 UP 3 Oklahoma 0.854 DOWN 4 Florida State 0.841 UP 5 Clemson 0.840 DOWN 6 LSU 0.800 UP 7 USC 0.770 UP 8 Michigan 0.767 DOWN 9 Stanford 0.761 UP 10 Washington 0.744 UP 11 Notre Dame 0.731 DOWN 12 Florida 0.728 UP 13 Tennessee 0.721 DOWN 14 Wisconsin 0.715 UP 15 Auburn 0.713 UP 16 Penn State 0.711 UP 17 Ole Miss 0.703 DOWN 18 TCU 0.702 UP 19 Georgia 0.701 UP 20 Louisville 0.695 UP 21 Oklahoma State 0.694 DOWN 22 Oregon 0.684 UP 23 UCLA 0.676 DOWN 24 Texas A&M 0.668 UP 25 Baylor 0.664 DOWN 26 North Carolina 0.663 DOWN 27 Miami (FL) 0.658 UP 28 Mississippi State 0.647 DOWN 29 Arkansas 0.643 DOWN 30 Michigan State 0.638 DOWN 31 Texas 0.634 UP 32 North Carolina State 0.630 UP 33 Iowa 0.629 DOWN 34 Washington State 0.620 UP 35 Pittsburgh 0.617 DOWN 36 Northwestern 0.604 UP 37 Houston 0.603 DOWN 38 Virginia Tech 0.603 UP 39 Brigham Young 0.601 DOWN 40 Boise State 0.600 DOWN 41 Utah 0.600 DOWN 42 Nebraska 0.588 DOWN 43 South Florida 0.586 DOWN 44 West Virginia 0.582 DOWN 45 Memphis 0.571 DOWN 46 Georgia Tech 0.566 UP 47 Kansas State 0.562 UP 48 San Diego State 0.562 DOWN 49 Western Kentucky 0.561 DOWN 50 Appalachian State 0.556 DOWN 51 Toledo 0.554 DOWN 52 California 0.553 DOWN 53 Texas Tech 0.553 DOWN 54 Arizona State 0.553 DOWN 55 Navy 0.542 DOWN 56 South Carolina 0.541 UP 57 Temple 0.528 DOWN 58 Arizona 0.527 DOWN 59 Duke 0.527 UP 60 Missouri 0.526 UP 61 Minnesota 0.525 DOWN 62 Indiana 0.525 UP 63 Syracuse 0.516 UP 64 Kentucky 0.508 UP 65 Western Michigan 0.506 DOWN 66 Vanderbilt 0.504 UP 67 Colorado 0.503 UP 68 Cincinnati 0.484 DOWN 69 Iowa State 0.476 UP 70 Wake Forest 0.471 UP 71 Virginia 0.462 UP 72 Colorado State 0.461 UP 73 Tulsa 0.460 UP 74 Southern Miss 0.457 DOWN 75 Bowling Green 0.456 DOWN 76 Boston College 0.454 UP 77 Maryland 0.454 UP 78 Oregon State 0.451 UP 79 Utah State 0.450 DOWN 80 Marshall 0.448 DOWN 81 Arkansas State 0.447 DOWN 82 Louisiana Tech 0.447 DOWN 83 Central Michigan 0.435 DOWN 84 Northern Illinois 0.433 DOWN 85 Air Force 0.426 DOWN 86 Illinois 0.425 DOWN 87 Georgia Southern 0.421 DOWN 88 Middle Tennessee 0.417 DOWN 89 Troy 0.405 UP 90 East Carolina 0.402 DOWN 91 Ohio 0.392 DOWN 92 Rutgers 0.391 DOWN 93 Purdue 0.390 DOWN 94 Southern Methodist 0.384 UP 95 UCF 0.365 UP 96 New Mexico 0.365 DOWN 97 San Jose State 0.358 DOWN 98 Nevada 0.355 DOWN 99 Wyoming 0.353 UP 100 Connecticut 0.344 DOWN 101 Florida Atlantic 0.343 DOWN 102 Ball State 0.335 UP 103 Georgia State 0.334 DOWN 104 Old Dominion 0.332 UP 105 Miami (OH) 0.332 UP 106 Akron 0.328 DOWN 107 UTSA 0.315 UP 108 Army 0.311 UP 109 Louisiana-Lafayette 0.311 UP 110 Tulane 0.310 UP 111 Fresno State 0.303 UP 112 Florida International 0.300 DOWN 113 UNLV 0.297 UP 114 South Alabama 0.297 UP 115 Massachusetts 0.293 UP 116 Eastern Michigan 0.289 UP 117 Idaho 0.286 DOWN 118 Kansas 0.285 UP 119 Buffalo 0.282 DOWN 120 Kent State 0.275 DOWN 121 Hawaii 0.270 UP 122 Rice 0.270 DOWN 123 New Mexico State 0.265 UP 124 North Texas 0.244 UP 125 UTEP 0.234 DOWN 126 Louisiana-Monroe 0.230 UP 127 Texas State 0.205 DOWN 128 Coastal Carolina 0.196 UP 129 Charlotte 0.193 UP 130 UAB 0.130 UP