Tag Archives: Sports

Michigan: Destined for an early exit?

Michigan is my favorite college basketball team, and for the first time in awhile, they are threatening to make a deep tournament run. However, they just lost three of four during a tough stretch against Indiana (L, away), Ohio State (W, home), Wisconsin (W, away), and Michigan State (W, away). I’m not writing them off — they only lost the away games — but some bad signs appeared in these games. Here’s the Game Stack for all four combined:

Mich 4 games 2-2013

Michigan looks good on turnovers, but that comes at a cost — they get crushed on free throws and two point percentage. Having watched the games, I can connect the dots for you: the Wolverines don’t drive to the hoop much against good teams. They have some great shooters who can get reasonably open (Trey Burke, Tim Hardaway, Jr.) who are happy to “settle” for jumpers.

This keeps the ball out of danger in the lane (low turnovers), but it means that Michigan never gets to the line and shoots a lower percentage on twos as well. Michigan also rebounds a lower percentage of their own misses than the opponent, which could be related — a lot of “second chances” are just put-backs after a shot close to the hoop.

So, is Michigan sunk? We’ll see. I have some faith that Mitch McGary can improve and find some high percentage twos down low, but right now, Michigan is probably not efficient enough offensively and not good enough on the boards to compete with the best teams in the country. I would worry less about four games if the problem was just poor shooting in a small sample, but the problem seems to be about playing style against good defenses. I don’t think that’s going to change.

If you’re interested, here are the Game Stacks for all four games. The trends I discussed are pretty consistent across the games:

Michigan at Indiana 2-2-2013 Ohio State at Michigan 2-5-2013

Michigan at Wisconsin 2-9-2013 Michigan at Michigan State 2-12-2013

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Basketball Stacks part 2: Rebounding

Yesterday, I posted a new idea for visualizing box scores: Game Stacks. While the first version did a good job of showing shooting percentages and turnover rates, it didn’t do a good job on rebounds. As my pops pointed out, Indiana had a big rebounding advantage over Michigan by the basic numbers (36-22), so it seemed wrong to rely only on the height of the stacks to determine who rebounded better. The reality: Michigan got more chances not because they rebounded better, but because they had more misses — and you have to miss to get a second chance. The height of the stacks just showed that Michigan got more offensive rebounds, even though their rebounding rate was terrible.

So, round two. Here’s the Michigan-Indiana Game Stack redesigned to capture rebounding:

Michigan at Indiana 2-2-2013

Without play by play data, I had to keep the rebounding simple — I figured out the offensive rebound rate for each team:

Off reb rate = your off rebs/(their def rebs + your off rebs).

Then, I multiplied this rate by the relevant number of shots to generate the “Missed (O Reb)” category for each type of shot (the dashed regions). Each dashed/empty combo now visualizes the offensive rebound rate for the relevant team.

Now the picture is clearer:

Visualization: Basketball Game Stacks

Note: On my dad’s advice, I posted another version of the Game Stacks that depicts rebounding rates, rather than just total offensive rebounds. The discussion in this post is a little naive on that point — the new version yields a better analysis of rebounding.

I have a general hang up when looking at the box score for basketball (or listening to announcers list off statistics). I see some rebounding numbers, but I can’t tell who rebounded better without offensive and defensive breakdowns, plus the number of shots missed by each team. And I see shooting percentages and shot attempts, but it’s hard to put it all together into how a team got its points.

I realized that what I really want to see is not complicated. Here’s the list:

  • What each team did with their scoring chances:
    • Two point attempts
    • Three point attempts
    • Free throw trips (2 attempts)
    • Turnovers
  • Efficiency on each type of shot
  • Rebounding advantage in terms of extra scoring chances
  • And, of course, total score

All these stats exist, but there should be an easy way to see all of it at once and get a sense for how the game was won. Here’s my first try, the Game Stack:

Michigan at Indiana 2-2-2013

The picture shows total “plays,” or chances to score, for each team, and total points, broken down by type. In a quick glance, you can see that Indiana was out-rebounded (Michigan got three more chances to score) and turned the ball over a ton. However, on just over 60 non-turnover plays, the Hoosiers Continue reading

Playoff Appetizer: True Wins Plus (Fumble Adjusted)

We might be halfway through the first quarter of the first NFL playoff game of 2013, but I’m still finishing up with baseball and just getting warmed up on football. Football month on the blog officially kicks off today — there’s lots of interest stuff to come, from innovative rule ideas and play calling to new prediction methods and game analysis. Today, I’m trying an addition to the measure of NFL team quality that I debuted last year: True Wins. True Wins are calculated as follows:

True Win = Blowout Wins + Close Wins/2 + Close Losses/2 + Ties/2

You may recognize the intuition from pythagorean expectations — you get full credit for blowout wins (I define this as more than 7 points), but no extra credit for winning by huge margins, and you get half credit for all close games, since those probably come down to luck more than skill. Last year, I showed that True Wins predicts a little better than pythagoreans, and it’s a whole lot more direct. Both measures are much better than using wins alone, which unfairly penalize (reward) teams that lose (win) a lot of close games.

What Else is Luck-Driven? Fumble Recoveries?

With the playoffs coming right up, I decided to try an improvement that adjusts for possible luck in fumble recoveries as well. Here’s the logic (from Football Outsiders):

Stripping the ball is a skill. Holding onto the ball is a skill. Pouncing on the ball as it is bouncing all over the place is not a skill. There is no correlation whatsoever between the percentage of fumbles recovered by a team in one year and the percentage they recover in the next year. The odds of recovery are based solely on the type of play involved, not the teams or any of their players . . . Fumble recovery is a major reason why the general public overestimates or underestimates certain teams. Fumbles are huge, turning-point plays that dramatically impact wins and losses in the past, while fumble recovery percentage says absolutely nothing about a team’s chances of winning games in the future. With this in mind, Football Outsiders stats treat all fumbles as equal, penalizing them based on the likelihood of each type of fumble (run, pass, sack, etc.) being recovered by the defense.

The keys are:

  1. Fumbles are huge turning points in games
  2. Teams don’t maintain high or low recovery rates over time

To quantify #1, I determined the point value of a recovery. A simple regression of point differential in each game on total fumbles and fumbles Continue reading

Is that a shiny new free agent in your stocking, or an old lump of coal?

NFL playoffs are right around the corner, but ’tis the season for a jolt of baseball excitement too, as teams sign new players. The contracts are getting bigger and bigger, supported by growing MLB revenues. Some of the major signings under the tree this year (more here):

  • Zack Greinke, 6 yrs, $147 million (Dodgers)
  • Josh Hamilton, 5 yrs, $125 million (Angels)
  • B.J. Upton, 5 yrs, $75 million (Rays)
  • Anibal Sanchez, 5 yrs, $80 million (Tigers)

But before you start thinking playoffs, remember that many big deals don’t work out. Who will be nice and who will be naughty this year?

The Old Lumps of Coal

From the list above, Greinke is 29 years old, Hamilton is 31, Upton is 28, and Sanchez is 28. Not many young players are available through free agency, but are these 4 to 6 year deals for 28 to 31 year olds a good idea? I tackled this question with my friend Jeff Phillips for ESPN the Magazine in early October.

Specifically, we wondered if long deals for 30 year olds made more sense during the steroid era, when players could recover, train, and maintain more easily. There are two sides of the coin: (1) how has older player performance changed, and (2) has older player compensation evolved appropriately. We focused on players in the top quarter of the salary distribution, since that’s where the big money is spent. To measure performance, we examined average Wins Above Replacement Player (WARP)* by age during and after the steroid era:

WARP bars

Uh oh. Although performance for all highly paid players has gone down, older “stars” have turned out to be coal indeed. Looking year by year highlights the post-PED age decline. Average WARP for older and younger stars was remarkably similar through the steroid era, but older player WARP Continue reading

New York is Lefty Land

I’m a Tigers fan, so I’m pretty excited about how things worked out the last week. Basically, everything went right for the Tigers and nothing went right for the Yankees.

The only glimmer of hope for the Yankees came in game one. Down 4-0, Ichiro Suzuki hit a line drive homer to right in the bottom of the ninth and Raul Ibanez followed with a pop fly two-run “shot” that might have been an out (or perhaps a double) in most parks. Hope turned to despair when Derek Jeter went down with an ankle injury in the 12th, ending his season, while the Tigers stormed back into the lead. Even worse for the Yankees, their near victory finally knocked Jose Valverde off his closer pedestal. The Tigers should have made that move months ago.

I want to go back to the homers though. It’s no coincidence that both homers went to right field off of left-handed bats. Here are the home/road home run splits for the Yankees lefties in 2012:

Continue reading

Sabermetrics: Cabrera vs. Trout, Round 2

Last week, I entered the fray on the Mike Trout versus Miguel Cabrera AL MVP debate. It’s similar to the 2010 AL Cy Young discussion — Felix Hernandez led the AL in strikeouts and ERA but managed just a 13-12 record because Seattle couldn’t score. The new era of baseball stats won out. Voters ignored wins, which have little to do with pitching quality, and Hernandez won the award.

Likewise, Trout lags Cabrera in highly publicized but somewhat meaningless  stats (RBI, Triple Crown). Some saber-men would have you believe that Trout laps Cabrera in the only stats that matter (WAR over 10 compared to 7 for Cabrera), but that requires a level of trust that I don’t have. WAR — Wins Above Replacement — is complicated to the point of complete confusion. Cabrera contributed more in some categories (doubles, homers, total bases, batting average) but less in others (triples, baserunning, defense). Is WAR capturing these contributions accurately?

True Runs Revised (A WAR Replacement)

Rather than critique WAR (which would take days), I developed a new, simpler stat: True Runs. True Runs (named in honor of my True Wins football statistic) estimates a player’s contribution to his team based only on simple statistics. I got some good comments on the methodology, and what better time to revise it than now, while listening to MVP chants ring out at Comerica Park in Detroit.

Per DRDR’s comment, I included outs/reached on error in the revised methodology:

  1. Using data since 1990, regress total runs scored by each team each season on total singles, doubles, triples, homers, walks, hit by pitches, usual outs/reached on error, strikeouts, double plays, stolen bases, and caught stealing in that season
  2. Take the coefficients from this regression, multiply them by each individual’s stats, and add up the result

Intuitively, the regression finds the best way to add up all these stats to most closely approximate total runs scored across all teams in all years. The result: True Runs now captures the four basic things a hitter can do at the plate — walk, get a hit, make an out/reach on an error, strikeout — as well as steals. The regression coefficients approximate how many runs each of these actions is worth, on average.*

Here’s the top 10 for 2012 across both leagues Continue reading