Tag Archives: Playoffs

The Quality of Postseason Play

Summary: I look at averages for hitters and pitchers in the postseason to see how their quality (relative to league average) has changed over time. Unsurprisingly, the gap between postseason and regular season average pitchers is larger than the comparable gap for hitters. The trend over time for pitchers is expected, with a decrease in quality relative to league average from the 1900s to mid-1970s and a slight increase since then that appears to be linked with the increased usage of relievers. The trend for hitters is more confusing, with a dip from 1950 to approximately 1985 and an increase since then. Overall, however, the average quality of both batters and pitchers in the postseason relative to league average is as high as it has been in the expansion era.


Quality of play in the postseason is a common trope of baseball discussion. Between concerns about optics (you want casual fans to watch high quality baseball) and rewarding the best teams, there was a certain amount of handwringing about the number of teams with comparatively poor records into the playoffs (e.g., the Giants and Royals made up the only pair of World Series teams ever without a 90 game winner). This prompted me to wonder about the quality of the average players in the postseason and how that’s changed over time with the many changes in the game—increased competitive balance, different workloads for pitchers, changes in the run environment, etc.

For pitchers, I looked at weighted league-adjusted RA9, which I computed as follows:

  1. For each pitcher in the postseason, compute their Runs Allowed per 9 IP during the regular season. Lower is better, obviously.
  2. Take the average for each pitcher, weighted by the number of batters faced.
  3. Divide that average by the major league average RA9 that year.

You can think of this as the expected result you would get if you chose a random plate appearance during the playoffs and looked at the pitcher’s RA9. Four caveats here:

  1. By using RA9, this is a combined pitching/defense metric that really measures how much the average playoff team is suppressing runs relative to league average.
  2. This doesn’t adjust for park factors, largely because I thought that adjustment was more trouble than it was worth. I’m pretty sure the only effect that this has on aggregate is injecting some noise, though I’m not positive.
  3. I considered using projected RA9 instead of actual RA9, but after playing around with the historical Marcel projections at Baseball Heat Maps, I didn’t see any meaningful differences on aggregate.
  4. For simplicity’s sake, I used major league average rather than individual league average, which could influence some of the numbers in the pre-interleague play era.

When I plot that number over time, I get the following graph. The black dots are observed values, and the ugly blue line is a smoothed rolling estimate (using LOESS). (The gray is the confidence interval for the LOESS estimate.)

Pitching

While I wouldn’t put too much weight in the LOESS estimate (these numbers should be subject to a large bit of randomness), it’s pretty easy to come up with a basic explanation of why the curve looks the way it does. For the first seventy years of that chart, the top pitchers pitched ever smaller shares of the overall innings (except for an uptick in the 1960s), ceding those innings to lesser starters and dropping the average quality. However, starting in the 1970s, relievers have covered larger portions of innings (covered in this FiveThirtyEight piece), and since relievers are typically more effective on a rate basis than starters, that’s a reasonable explanation for the shape of the overall pitcher trend.

What about hitters? I did the same calculations for them, using wOBA instead of RA9 and excluding pitchers from both postseason and league average calculations. (Specifically, I used the static version of wOBA that doesn’t have different coefficients each year. The coefficients used are the ones in The Book.) Again, this includes no park adjustments and rolls the two leagues together for the league average calculation. Here’s what the chart looks like:

Batting

Now, for this one I have no good explanation for the trend curve. There’s a dip in batter quality starting around integration and a recovery starting around 1985. If you have ideas about why this might be happening, leave them in the comments or Twitter. (It’s also quite possible that the LOESS estimate is picking up something that isn’t really there.)

What’s the upshot of all of this? This is an exploratory post, so there’s no major underlying point, but from the plots I’m inclined to conclude that, relative to average, the quality of the typical player (both batter and pitcher) in the playoffs is as good as it’s been since expansion. (To be clear, this mostly refers to the 8 team playoff era of 1995–2011; the last few years aren’t enough to conclude anything about letting two more wild cards in for a single game.) I suspect a reason for that is that, while the looser postseason restrictions have made it easier for flawed teams to make it in the playoffs, they’ve also made it harder for very good teams to be excluded because of bad luck, which lifts the overall quality, a point raised in this recent Baseball Prospectus article by Sam Miller.


Two miscellaneous tidbits from the preparation of this article:

  • I used data from the Lahman database and Fangraphs for this article, which means there may be slight inconsistencies. For instance, there’s apparently an error in Lahman’s accounting for HBP in postseason games the last 5 years or so, which should have a negligible but non-zero effect on the results.
  • I mentioned that the share of batters faced in the postseason by the top pitchers has decreased steadily over time. I assessed that using the Herfindahl-Hirschman index (which I also used in an old post about pitchers’ repertoires.) The chart of the HHI for batters faced is included below. I cut the chart off at 1968 to exclude the divisional play era, which by doubling the number of teams decreased the level of concentration substantially.  HHI
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A Reason Bill Simmons is Bad At Gambling

For those unaware, Bill Simmons, aka the Sports Guy, is the editor-in-chief of Grantland, ESPN’s more literary (or perhaps intelligent, if you prefer) offshoot. He’s hired a lot of really excellent  writers (Jonah Keri and Zach Lowe, just to name two), but he continues to publish long, rambling football columns with limited empirical support. I find this somewhat frustrating given that the chief Grantland NFL writer, Bill Barnwell, is probably the most prominent data-oriented football writer around, but you take the good with the bad.

Simmons writes a column with NFL picks each week during the season, and has a pretty so-so track record for picking against the spread, as detailed in the first footnote to this article here. Simmons has also written a number of lengthy columns attempting to construct a system for gambling on the playoffs, and hasn’t done too great in this regard either. I’ve been meaning to mine some of these for a post for a while now, and since he’s written two such posts this year already (wild card and divisional round), I figured the time was right to look at some of his assertions.

The one I keyed on was this one, from two weeks ago:

SUGGESTION NO. 6: “Before you pick a team, just make sure Marty Schottenheimer, Herm Edwards, Wade Phillips, Norv Turner, Andy Reid, Anyone Named Mike, Anyone Described As Andy Reid’s Pupil and Anyone With the Last Name Mora” Isn’t Coaching Them.

I made this tweak in 2010 and feel good about it — especially when the “Anyone Named Mike” rule miraculously covers the Always Shaky Mike McCarthy and Mike “You Know What?” McCoy (both involved this weekend!) as well as Mike Smith, Mike “The Sideline Karma Gods Put A Curse On Me” Tomlin, Mike Munchak and the recently fired Mike Shanahan. We’re also covered if Mike Shula, Mike Martz, Mike Mularkey, Mike Tice or Mike Sherman ever make comebacks. I’m not saying you bet against the Mikes — just be psychotically careful with them. As for Andy Reid … we’ll get to him in a second.

That was written before the playoffs—after Round 1, he said he thinks he might make it an ironclad rule (with “Reid’s name…[in] 18-point font,” no less).

Now, these coaches certainly have a reputation for performing poorly under pressure and making poor decisions regarding timeouts, challenges, etc., but do they actually perform worse against the spread? I set out to find this out, using the always-helpful pro-football-reference database of historical gambling lines to get historical ATS performance for each coach he mentions. (One caveat here: the data only list closing lines, so I can’t evaluate how the coaches did compared to opening spreads, nor how much the line moved, which could in theory be useful to evaluate these ideas as well.) The table below lists the results:

Playoff Performance Against the Spread by Select Coaches
Coach Win Loss Named By Simmons Notes
Childress 2 1 No Andy Reid Coaching Tree
Ditka 6 6 No Named Mike
Edwards 3 3 Yes
Frazier 0 1 No Andy Reid Coaching Tree
Holmgren 13 9 No Named Mike
John Harbaugh 9 4 No Andy Reid Coaching Tree
Martz 2 5 Yes Named Mike
McCarthy 6 4 Yes Named Mike
Mora Jr. 1 1 Yes
Mora Sr. 0 6 Yes
Phillips 1 5 Yes
Reid 11 8 Yes
Schotteinheimer 4 13 Yes
Shanahan 7 6 Yes Named Mike
Sherman 2 4 Yes Named Mike
Smith 1 4 Yes Named Mike
Tice 1 1 Yes Named Mike
Tomlin 5 3 Yes Named Mike
Turner 6 2 Yes

A few notes: first, I’ve omitted pushes from these numbers, as PFR only lists two (both for Mike Holmgren). Second, the Reid coaching tree includes the three NFL coaches who served as assistants under Reid who coached an NFL playoff game before this postseason. Whether or not you think of them as Reid’s pupils is subjective, but it seems to me that doing it any other way is going to either turn into circular reasoning or cherry-picking. Third, my list of coaches named Mike is all NFL coaches referred to as Mike by Wikipedia who coached at least one playoff game, with the exception of Mike Holovak, who coached in the AFL in the 1960s and who thus a) seems old enough not to be relevant to this heuristic and b) is old enough that there isn’t point spread data for his playoff game on PFR, anyhow.

So, obviously some of these guys have had some poor performances against the spread: standouts include Jim Mora, Sr. at 0-6 and Marty Schottenheimer at 4-13, though the latter isn’t actually statistically significantly different from a .500 winning percentage (p = 0.052). More surprising, given Simmons’s emphasis on him, is the fact that Reid is actually over .500 lifetime in the playoffs against the spread. (That’s the point estimate, anyway; it’s not statistically significantly better, however.) This seems to me to be something you would want to check before making it part of your gambling platform, but that disconnect probably explains both why I don’t gamble on football and why Simmons seems to be poor at it. (Not that his rule has necessarily done him wrong, but drawing big conclusions on limited or contradictory evidence seems like a good way to lose a lot of money.)

Are there any broader trends we can pick up? Looking at Simmons’s suggestion, I can think of a few different sets we might want to look at:

  1. Every coach he lists by name.
  2. Every coach he lists by name, plus the Reid coaching tree.
  3. Every coach he lists by name, plus the unnamed Mikes.
  4. Every coach he lists by name, plus the Reid coaching tree and the unnamed Mikes.

A table with those results is below.

Combined Against the Spread Results for Different Groups of Coaches Cited By Simmons
Set of Coaches Number of Coaches in Set Wins Losses Winning Percentage p-Value
Named 14 50 65 43.48 0.19
Named + Reid 17 61 71 46.21 0.43
Named + Mikes 16 69 80 46.31 0.41
All 19 80 86 48.19 0.70

As a refresher, the p-value is the probability that we would observe a result as or more extreme as the observed result if there were no true effect, i.e. the selected coaches are actually average against the spread. (Here’s the Wikipedia article.) Since none of these are significant even at the 0.1 level (which is generally the lowest barrier to treating a result as meaningful), we wouldn’t conclude that any of Simmons’s postulated sets are actually worse than average ATS in the playoffs. It is true that these groups have done worse than average, but the margins aren’t huge and the samples are small, so without a lot more evidence I’m inclined to think that there isn’t any effect here. These coaches might not have been very successful in the playoffs, but any effect seems to be built into the lines.

Did Simmons actually follow his own suggestion this postseason? Well, he picked against Reid, for Mike McCoy (first postseason game), and against Mike McCarthy in the wild card round, going 1-0-2, with the one win being in the game he went against his own rule. For the divisional round, he’s gone against Ron Rivera (first postseason game, in the Reid coaching tree) and against Mike McCoy, sticking with his metric. Both of those games are today, so as I type we don’t know the results, but whatever they are, I bet they have next to nothing to do with Rivera’s relationship to Reid or McCoy’s given name.