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Showing posts with label MLB. Show all posts
Showing posts with label MLB. Show all posts

Wednesday, May 27, 2015

MLB Power Rankings: May 27th, 2015

With methods described in this post, here are the latest MLB power rankings!


TPI = Team Performance Index (my ranking metric).  Based on wRC, DRA, DRS, and UZR.
W% = Team Winning Percentage (i.e. real life)
Py% = Pythagorean Winning Percentage (based on real RS and RA)

On-Paper Playoff Leaders

American League - East: Rays, Central: Tigers, West: A's, Wild cards: Royals and Blue Jays
National League - East: Nationals, Central: Cardinals, West: Dodgers, Wild cards: Giants and Cubs


Notes

The Reds and White Sox were the big fallers this past week+ in terms of TPI, with both teams basically cratering.  I'm still pretty surprised at how bad the White Sox have been.  The Reds will hopefully rebound, a bit, but are still only a bit under their preseason projections.  Their decline this past week dropped them out of the TPI-based wild card, which now goes to the Cubs.  The Braves also faltered badly, although I like the Juan Uribe trade quite a lot for the Braves...short term, at least.

Meanwhile, the big risers were the Pirates.  They've been on a tear lately, climbing over .500 for the first time in a long time, and now just two games behind the Cubs.  It would be kind of neat to see those two teams ultimately claim the two NL wild cards, although the Giants certainly will have something to say about that.

Monday, May 18, 2015

MLB Power Rankings - As of 18 May, 2015

During the 2009 and 2010 seasons, I ran a series of power ranking at Beyond the Box Score.  They progressively became more and more complicated, but the initial premise was fairly straightforward.  Rather than looking at actual wins, or even actual runs scored or runs allowed, I estimated runs scored and runs allowed from their components and used those scores to estimate winning percentage.  The result was a ranking of teams based on the same component statistics that we use to evaluate individual players.

At the urging of no one, I've reconstituted the power rankings for this season.*  Details on calculations are below, but I don't want to bury the lede: here are the official OB&tR Power Rankings for 18 May, 2015!

* Or maybe, for this post only!  We'll see!


TPI = Team Performance Index (my ranking metric).  Based on wRC, DRA, DRS, and UZR.
W% = Team Winning Percentage (i.e. real life)
Py% = Pythagorean Winning Percentage (based on real RS & RA)

On-Paper Playoff Leaders

American League - East: Rays, Central: Tigers, West: A's, Wild cards: Royals & Blue Jays
National League - East: Nationals, Central: Cardinals, West: Dodgers, Wild cards: Giants & Reds

Rated Better Than Expected

If you compare Pythagorean records to TPI records, these are the teams that come out as faring especially well by TPI:
Tigers: +.094
Indians: +.091
Reds: +.086
Giants: +.069
Athletics: +.056
It's not lost on me that the Reds are on this list.  I had no idea they would do well on this until after I finished the spreadsheet, honest!

Why have these teams rated highly?  Well...it's not any one reason.  Some of these teams were predicted to score a lot more runs than they actually have (Tigers +23 runs, Giants +30 runs).  Some were predicted to have allowed far fewer runs than they actually have (Indians -28 runs(!), Athletics -23 runs).  Someare outstanding fielding teams (Reds & Tigers at +12 and +14), while some are very poor fielding teams (Indians -15 runs, Athletics -13 runs).  

Now, the latter two (Indians & Athletics) show up in both fielding and pitching gap list, so maybe the method isn't sensitive enough to teams with extraordinarily bad fielding?  If that were the case, we'd expect some kind of relationship between fielding numbers and the gap between TPI and pythagorean records, but but there isn't one:
There appears to be a bit of a weird arc in the data, but if there were one data point at around (0,-0.05), we wouldn't be saying that.  I'm just not convinced that fielding causes any kind of systematic bias in the data.  And look: the Royals (at +33 runs!) are right at their Pythagorean expectation.

So, maybe these teams have just gotten unlucky.  Or something.  I certainly would expect that the difference between TPI and Pythagorean record will close as the season goes on.  Which is more predictive is an open question.

Rated Worse than Expected

These teams are rated much worse by TPI than their Pythagorean record:
Mets: -.139
Angels: -.118
Pirates -.108
Astros: -.063
Twins: -.057

All of these teams are average-fielding teams.  Some have hit better than estimated (Twins +20, Mets +16).  Some have pitched or fielded better than estimated (Mets -30, Pirates -27).  So, again, no clear pattern.


...In any case, there you have it.  I find this kind of thing interesting to track, so I'll likely update it from time to time throughout the season.

Calculation details below the jump!

Sunday, June 15, 2014

Replay Is Working, but Not For Reds

The implementation of expanded replay has come under a fair amount of fire this year.  We've all seen a scenario like this: after the obligatory dawdle while his video guy looks at the play, the manager challenges a ruling on the field, and we all watch a number of angles at home while the umpire at MLBAM makes his decision.  The replays seem to clearly show that the call was incorrect...but, after a minute or two of a wait, the call in the field is upheld.  The reason?  No clear and indisputable evidence that the call was wrong, despite it being pretty clear that it actually is.

This scenario has both hurt and helped the Reds this year, but either way it is maddening to watch.  Replay is supposed to help get calls right.  If it's upholding the wrong call, then what is the point of going through this?

On this point, I saw this tweet come across my twitter feed a few days ago:
I thought it was a really good point.  Replay isn't perfect.  But with every overturned call, we're seeing mistakes being fixed that otherwise would have been upheld.  Umpires still blow calls, and replay still fails to correct some of them.  But we've already seen hundreds of incorrect calls fixed this season.  Hundreds!

The link in that post is to Baseball Savant, which is happily tracking all of the replay challenges this year via pitchf/x data.  It's a great resource.  As of now, the number of overturned calls has risen to 246 calls, which is 47% of all challenges.  Here's how that number has evolved this year:


The blue line is a cumulative average for the entire season.  What we can see is that the rate of calls being overturned started at or around 40% in April, but then rose steadily through May and has flat-lined around the current 47%.

The 9-day moving average is a bit more revealing.  It seems as though the replay umpires began the season being pretty conservative for the first two weeks.  Then, they started to routinely overturn just over half of the calls from that point on...with a strange dip that occurred at the end of May/beginning of June (~23 May through 3 June).  I don't know what happened during that dip, but the rate of overturned calls dropped down to 30% for a brief period.  I'm going to speculate that the umpires are still making adjustments to their internal criteria for when to overturn calls.  Therefore, we might have seen a short-term adjustment to be more conservative, which was then quickly relaxed.


Replays and the Reds

So, if the MLB average is that just shy of half of all challenges are successful in getting a call overturned, why does it seem like the Reds almost always lose their challenges?

Probably, it's because the Reds have almost always lost their challenges.  The Reds are tied for the fewest challenges in all of baseball with 9 (the highest are the Cubs & Rays with 23 each).  They have won just 2 of their challenges (22%).  In contrast, the teams the Reds have played have made 14 challenges against the Reds, and have won 8 of them (57%).  Replay has not been kind to the Reds thus far.

Who is to blame?  The Reds' video guy?  Small sample sizes?  Bad luck?  I'd lean toward the latter explanations.  But...yeesh, let's hope their luck changes soon.

Thursday, May 08, 2014

Swings and Misses: Surprising Changes in 2014 Hitter Strikeout Rates

Todd Frazier is striking out far less often in 2014.
Photo Credit: Andrew Mascharka
It's May.  And when it comes to trying to draw anything from individual player statistics, we're still in small sample size land..  But short of just reporting what players have done thus far, most of us are at the point that we want to start using the 2014 data to actually infer something about changes in talent.  But with most everyday players only logging between 100 and 150 PA's, what can we actually say?

In Pizza Cutter's now-classic study (which he re-did here), he found that the first statistics to stabilize in a season--meaning the first stats that can provide some meaning beyond the noise--was strikeout rate.  Specifically, he found that when you have 60 PA's, you can use those PA's to explain roughly 50% of the variation in that player's next 60 PA's.  All other hitting statistics are more volatile than that, requiring even more PA's to get a large enough sample to provide that level of prediction.  

Strikeout rate is a funny thing for hitters, because higher strikeout rates are often correlated with increased performance when you compare across players, rather than decreased performance.  Yes, strikeouts are generally bad things.  But players who strike out more tend to be more willing to take pitches they can't handle (this leads to more walks, and sometimes better BABIPs), and tend to swing harder (this leads to more power).  

Nevertheless, given that most everyone has 60 PA's by now, I thought it would be interesting to look at who has seen the biggest shifts in strikeout rate across major league baseball...and how that has correlated to their performance thus far.  I'm only looking at players that have more than Pizza Cutter's requisite 60 PA's, and am comparing their 2014 strikeout rate to their projected strikeout rate (based on an average of their Oliver, Steamer, and ZIPS preseason projections...rarely did those systems show substantial differences among them, but I decided to take the average anyway).

Largest Increases in Strikeout Rate Across MLB


This is the group that people tend to worry about: players who experience a big spike in their strikeout numbers.  And we see a lot of cases like that on this list.  Junior Lake, for example, has been striking out an absurd 42% of the time for the Cubs...and there's no question that he's struggled.  Pablo Sandoval--who I thought was supposed to have a good year based on his offseason weight pictures?--has similarly seen a big spike in his strikeout rates, and has had a horrific start to the season.  

But there are some others on this list who it's hard to get too concerned about.  Mike Trout is second only to the amazing Troy Tulowitzki (my long-time man-crush) at the top of the WAR leaderboards, and there's a sizeable gap between him and the rest of the field.  He's striking out more this season...but honestly, is anyone worried about him?  Justin Upton, similarly, is off to another great start this year.  Sure, his BABIP and HR/FB rates are probably not sustainable, but that's to be expecting when you're wOBAing 0.420.  I don't see anyone sounding alarms about his increased strikeout rate.

Overall, it is the case that more players on this list--and remember, these are the extremes--have underperformed than overperformed, to the tune of about 0.025 wOBA points.  As a group, these players were expected to be slightly above average (0.319 wOBA), but they've been below-average thus far.  So the lesson?  A large spike in strikeout rate can be reason to be concerned about a player, but it's also not a reason to panic.


Largest Decreases in Strikeout Rate Across MLB


If the first list had players we should worry about, this is the group that many of us will want to get excited over.  And at the top of this list, at least, you do see some of the early season's most exciting and surprising performances.  Charlie Blackmon has been a revelation in Colorado, and has gone from solid to brilliant in ability to avoid the strikeout while performing at MVP-caliber levels.  Miguel Montero similarly looks to be rejuvenated this year, and Derek Norris seems to be breaking out.  

Most players on this list, however, have shown more modest changes in performance.  Wilin Rosario, Jeff Baker, and Travis d'Arnaud are struggling despite making more contact.  Players like Pedro Alvarez, Brandon Moss, and Ike Davis--guys who have historically had strikeout "problems" aren't yet showing any increase in their performance as a result of their apparent increase in ability to avoid the strikeout rate.  Overall, players with a dramatically reduced strikeout rate in the season are showing only a slight increase in their overall performance this year (an increase of ~0.014 wOBA units).

Overall Relationship: do changes in strikeout rates lead to changes in performance?

With the caveat that wOBA is not at all stable right now, I did do a quick comparison between the difference in projected and actual strikeout rate versus the difference between projected and actual wOBA.  Here's what I found:


So, it's not a strong effect.  If I was doing this right--on full-season data, for example--it would be a stronger effect, because wOBA would have stabilized better.  But even now, we can see a small, predictive effect: unexpectedly low strikeouts leads to higher wOBA, and vice versa.  The correlation is -0.26.

2014 Cincinnati Reds Hitter Strikeout Rates



For all of the complaints that I keep hearing about the Reds offense, on the whole the team's regulars have hit better than projected, wOBAing 0.342 thus far compared to 0.319 expected.  Most of that is driven by the stupendous performances of that catching corps: Brayan Pena and Devin Mesoraco have been amazing.  I'm also not weighting the averages above by PA's (due to independence concerns), so they are probably (definitely) having too much impact on those totals.

Still, most Reds players are right about where you'd expect them to be based on preseason their projections.  Brandon Phillips pops up as the only Reds player to see more than a 5% increase in his strikeout rate: a subject that C. Trent Rosecrans wrote about recently.  He's actually hitting better since C. Trent's article (see May split) but Phillips has done little this year to suggest that his struggles last year were entirely driven by injury.  I'm hopeful that he can still be productive, but he's probably the hitter that I'm most worried about on the Reds (and I'm not the only one).  My little pseudo-study here of 2014 players, however, at least suggests that a spike in strikeouts is not necessarily indicative of disaster.

On the other side of the coin is Todd Frazier.  While he just hits the + 5% cutoff I arbitrarily set for a "green dot" in my spreadsheet, he's seeing virtually the same change in his strikeout rate as Brandon Phillips...just in the better direction!  I haven't seen Frazier getting a lot of attention for his very nice start thus far, but it's been great to see him anchor down the hot corner.  Between his offense and his excellent defense, it's no surprise that Frazier is at the top of the Reds' WAR leaderboard.  Based on his performance thus far, he's been the Reds' MVP!

Given how unreliable wOBA is right now, I don't think we can conclusively link the changes in the players' performances to their changes in strikeout rates.  But I'm comfortable saying, at the least, that when a player has a sharp increase in his strikeout rate in a month's time (~60-100 PA's), that is reason to be somewhat concerned.  And when a player shows a sharp drop in strikeout rate, there is reason to be optimistic.  

So with that in mind, all signs look good on Todd Frazier.  And, unfortunately, the opposite is true of Brandon Phillips.