Average Hit Band: Photograph of the DRC Era’s New Normal

This MLB offseason, while arguably a bit chilly by hot stove standards, did offer baseball fans a hot new hitting metric in Baseball Prospectus’ Deserved Runs Created Plus (DRC+). In the words of its creators, DRC+ is “designed to parse out more accurately . . . batters’ expected individual contributions — separate from all other player and environmental factors — to their teams’ offensive production.” (My summary of that introductory article, which was nominated for a SABR research award, can be found through here.)

Unlike traditional, rate-based hitting metrics such as batting average (BA) and on-base percentage (OBP), DRC+ is an index statistic, meaning that it’s arranged to indicate the degree to which a player is above or below average, where 100 represents average. As part of its DRC+ rollout, BP published an homage to rate statistics (link and summary available through here) that touts their simple approach to delivering contextual information.

This undoubtedly is a user advantage for metrics like DRC+, but, by placing the focus so squarely on the average reference point, the initial transition from the rate-stat world of BA/OBP/SLG to the index-stat world of DRC+ can be a little bit rough. To help smooth things, I thought it would be beneficial to illustrate the translation with a quick look at all of the hitters who had “average,” according to DRC+, seasons at the plate in 2018.

Last season, eleven batters finished with at least 275 plate appearances and DRC+ marks of 100. As their traditional slash lines illustrate, they got to that point in a variety of ways.

The ranges for these eleven on each of the traditional hitting rate statistics are:

  • BA: .224 – .280
  • OBP: .294 – .351
  • SLG: .359 – .484

Obviously, because of the multitude of factors DRC+ considers, including both player-performance factors and environmental factors, these rate bands only serve as rough guidelines for fans making the mental shift from the rate world of BA/OBP to DRC+ that want a little help finding their bearings. (Also keep in mind that these “average” slash-line bands will vary from year to year. For example, in 1998, there were four players with at least 275 PA who posted DRC+ marks of 100, Matt Williams, Devon White, Luis Alicea, and Robin Ventura: BA between .263 and .279; OBP between .327 and .372; and SLG between .425 and .456. For reference, Mark McGwire, .299/.470/.752, led MLB with a DRC+ of 211 that year.)

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Previously
Miguel Cabrera continues to shine in the DRC era
Miguel Cabrera further bolstered by sabermetric update
Trout vs. Cabrera, and Aging with DRC+ (via Baseball Prospectus)

Related
The Best Baseball Research of the Past Year (2018)

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The Best Baseball Research of the Past Year

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Once again, the Society for American Baseball Research has chosen fifteen (non-ALDLAND) finalists for awards in the areas of contemporary and historical baseball analysis and commentary.

My latest post at Banished to the Pen highlights each finalist. The winners will be announced on Sunday.

The full post is available here.

Miguel Cabrera continues to shine in the DRC era

Last month, I wrote about the substantial change in the way Baseball Prospectus is measuring hitter value and the significance of that change to Miguel Cabrera’s statistical legacy. Yesterday morning, BP announced “updates” to its hitter-value metric, DRC+. The description of the updates is pretty technical, and I commend you to the linked article if you want to get into the nuts and bolts, to the extent BP exposes them to the public. The short story seems to be that the original version of DRC+ undervalued two types of players: 1) those who play many of their games in “extreme ballparks” (Coors Field is the only one I’ve seen mentioned in the early DRC+ critiques and the update article, but I assume others are included) and 2) “extreme”-output hitters who do one thing really well (the examples I’ve seen discussed usually include singles hitters like Tony Gwynn and Ichiro Suzuki).

For Cabrera, the update credited him with even more productive value, adding almost two wins to his career total. The following chart, which I’ve adapted from the one I created for the BttP article, compares Cabrera’s career and season-by-season win totals under three different WARP regimes: a) TAv-based WARP; b) the original DRC+-based WARP; and c) the updated DRC+-based WARP.

cabrera warp drc update

(Notes: TAv-based WARP isn’t available for 2018, which affects the WARP totals in the bottom row. Orange highlighting signals seasons in which TAv and original DRC+ disagree about whether Cabrera’s offense was above or below average. Updated DRC+ was consistent with original DRC+ in that respect.)

Looking first at the table’s seventh column, the DRC+ update added to Cabrera’s totals, not infrequently by double digits, in every season save two minor decreases in 2007 and 2014. Looking next to the table’s final column, though, there isn’t really a consistent correlation between either the direction or magnitude of the update’s DRC+ adjustments and WARP; indeed, in 2008 and 2012, the update resulted in increases in Cabrera’s DRC+ but decreases in WARP. As the totals in the bottom row indicate, however, overall, the DRC+ update boosted Cabrera’s career WARP total by 1.8 wins. Not bad.

Here I will add the same caveat I included in my previous article on this subject, which is that I don’t have a deep enough understanding of DRC+, a proprietary metric, to explain with any further detail why this happened. (I also will note that, because BP does not archive its statistical reports from prior metric regimes, the foregoing is reliant on data previously captured by Archive.org’s Wayback Machine and me.)

What outsiders like us can say is that the Deserved-Runs-Created era has been good to Cabrera, from validating his MVP wins over Mike Trout to restoring all of his season-by-season WARP numbers into the black to, following yesterday’s update, increasing his career WARP total. None of this is likely to stir any concern on the parts of Al Avila or Chris Ilitch that Cabrera suddenly is on track to challenge for MVP votes in 2023 such that his $30 million option for his age-forty-one season in 2024 will vest, but the growing– even if by very small amounts– recognition of Cabrera’s past achievements is nice to see.

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Previously
Miguel Cabrera further bolstered by sabermetric update
Trout vs. Cabrera, and Aging with DRC+ (via Baseball Prospectus)

Trout vs. Cabrera, and Aging with DRC+ (via Baseball Prospectus)

MLB: All Star GameIt was about as clear as these things get, and the writers got it wrong. In fact, they got it wrong twice. That was the consensus, in our sabermetric corner of the internet, when Miguel Cabrera stole consecutive MVP awards from Mike Trout in 2012 and 2013.

Cabrera was a lumbering first baseman, shoved across the diamond only because the Tigers decided to force-fit Prince Fielder onto their plodding roster. He was a great hitter, but he added no value beyond that hitting. Trout, at the tender ages of 20 and 21, lit up the field in ways Cabrera couldn’t. He robbed home runs in center field, stole bases both often and efficiently, was one of the most consistent hitters in baseball, and according to the best information we had at the time, he was also Cabrera’s equal (or very nearly so, or perhaps even his superior) at the plate.

Baseball-Reference and FanGraphs each had Trout about 3.0 WAR better than Cabrera in 2012, and about 1.5 WAR better than him in 2013. We had the gap slightly smaller in 2012, but slightly larger in 2013. When such a clear gap between the best player and the field exists, it’s rare that the award goes to the “wrong” one. In this case, though, more or less everyone with a stat-savvy bone in their body espoused the belief that it had happened.

We were, all of us, deceived. … Read More

(via Baseball Prospectus)

One-Paragraph Preview: 2018 NLDS – Dodgers-Braves

ozzie

The Los Angeles Dodgers and Atlanta Braves begin their best-of-five National League Divisional Series tonight at Chavez Ravine. There are dozens of articles on reputable baseball websites previewing this series, all of which will leave you with the impression that the Dodgers are the superior team and probably but not definitely will prevail in this round. I write separately for the sole purpose of highlighting a conflict in the literature. Baseball Prospectus’ projection system, PECOTA, gives Los Angeles a solid (57%/43%) advantage. Interestingly, a postseason-prediction formula sabermetric forefather Bill James originally developed in 1972 that Rob Mains recently updated likes the Braves in this round. My own view is that it’s difficult to find an exploitable weakness in any phase of the Dodgers’ game, but I can’t count the Braves out in a competitive developmental environment that, more and more, seems to run on Coughlin Time. First pitch is set for 8:37 pm on MLB Network.

Baseball Notes: Offensive Discrimination

baseball notes

Although they may continue to cite them because of their familiarity as reference points, baseball analysts largely have moved on from the historically conventional hallmarks of pitcher and batter performance– ERA and batting average (“BA”), respectively– in favor of more comprehensive metrics that provide a more accurate picture of player performance by addressing some of those traditional statistics’ blind spots.

Focusing here on hitters, some of BA’s most notable blind spots include walks; the fact that each park has different dimensions; and the significant variance in the values of different types of hits (e.g., a single versus a home run). As they have with WAR, the three main baseball-analytics websites each offer their own improved versions of BA: Baseball Prospectus’ True Average (“TAv”); Baseball-Reference’s adjusted on-base-plus-slugging (“OPS+”); and FanGraphs’ Weighed Runs Created Plus (“wRC+”). Visually, TAv looks like a batting average but is scaled every year such that an average hitter has a TAv of .260, while OPS+ and wRC+ are scaled to an average of 100.

If you’ve read baseball articles here or at those websites, then you’ve seen those metrics cited, sometimes seemingly interchangeably, in the course of an examination of hitting performance. As BP’s Rob Mains notes in the first part of a recent two-part series at that site, there’s good reason to treat these three metrics similarly: they all correlate very strongly with each other. (In other words, most batters who are, for example, average according to TAv (i.e., .260) also are average according to OPS+ and wRC+ (i.e., 100).)

There are differences between the three, however, and those differences arise because each regards the elements of batting performance slightly differently. As Rob explained:

How the three derive the numbers themselves, including their respective park factors, is pretty small ball. Bigger ball, though, it what goes into them.

  • OPS+ incorporates the same basic statistics as OPS: At-bats, hits, total bases, walks, hit by pitches, and sacrifice flies.
  • wRC+ weights singles, doubles, triples, home runs, walks, and HBPs, with the weighting changing from year to year. For example, a home run had a weight of 2.337 in 1968 but only 1.975 in 1996, reflecting the scarcity of runs in the former year. Additionally, wRC+ considers only unintentional walks.
  • TAv also weights outcomes, including strikeouts (slightly worse than other outs) and sacrifices (slightly better than other outs). TAv also includes batters reaching base on error and incorporates situational hitting[, which refers to hitting that occurs only when runners are on base: Sacrifice hits, sacrifice flies, and hitting into double and triple plays].

So while all three measures look at the same thing—hitting—they’re not doing it quite the same. For OPS+, a walk is as good as a hit, from an OBP perspective, and a home run is four times as good as a single, per SLG. FanGraphs’ wRC+ weights them, but it doesn’t weight outs, as TAv does. Only TAv considers situational hitting.

When applied to players who are especially good or bad in those areas where the three metrics diverge, the result is a lack of correlation between the three with respect to that player. (Cf. the divergent views of the three WAR metrics with respect to Robbie Ray.) Mains’ second article examines some of those players of whom TAv, OPS+, and wRC+ take different views (e.g., Barry Bonds, Kris Bryant, Ian Kinsler, and David Ortiz) before explaining a few general conclusions:

[TAv, OPS+, and wRC+ are] very similar. You can use any of them and feel confident that you’re usually capturing the key characteristics of a batter.

If you want to drill down, though, here are the differences I found:

  • The lack of weighting in OPS+ means that it gives slightly less weight to singles and slightly more weight to home runs and walks than TAv and wRC+.

  • TAv’s inclusion of situational hitting means that batters who are extremely good or bad at avoiding double plays are going to get rewarded or penalized. (Situational hitting also includes bunting, but nobody does that anymore anyway.)

  • The black box factor in these calculations is park factors. Each of the three sites calculates them their own way. They can account for some changes, though not in a predictable or transparent way like high walk totals or low GIDP rates can.

I expect I’ll continue to use these three metrics somewhat interchangeably in articles at this site, although my preexisting (mostly uneducated) preferences for TAv and wRC+ likely will continue. Articles like Rob’s serve as both an important reminder that, at the edges, these updated metrics aren’t exactly the same and an entry point into thinking more precisely about what we ourselves value in the process of evaluating hitter performance.

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Previously
Baseball Notes: Current Issues Roundup
Baseball Notes: Baseball’s growth spurt, visualized

Baseball Notes: The WAR on Robbie Ray
Baseball Notes: Save Tonight
Baseball Notes: Current Issues Roundup
Baseball Notes: The In-Game Half Lives of Professional Pitchers
Baseball Notes: Rule Interpretation Unintentionally Shifts Power to Outfielders?
Baseball Notes: Lineup Protection
Baseball Notes: The Crux of the Statistical Biscuit
Baseball Notes: Looking Out for Number One
Baseball Notes: Preview

The Best Baseball Research of the Past Year

Once again, the Society for American Baseball Research has chosen fifteen (non-ALDLAND) finalists for awards in the areas of contemporary and historical baseball analysis and commentary.

My latest post at Banished to the Pen highlights each finalist. The winners will be announced on Sunday.

The full post is available here.

The Best Baseball Research of the Past Year

Once again, the Society for American Baseball Research has chosen fifteen (non-ALDLAND) finalists for awards in the areas of contemporary and historical baseball analysis and commentary, and they are holding a public vote to determine the winners.

My latest post at Banished to the Pen highlights each finalist and includes a link to cast your vote to help determine the winners.

Read the full post, which includes summaries of each of the fifteen nominated pieces; reveals my ballot; and includes some general comments on this year’s selections here.

2016 Oregon is the Oregon Everyone Thought They Were Watching for the Last Decade

There is a myth that exists in college football that some really good teams are great offenses with bad defenses. These teams win games by scores like 62-51 or 45-38, and, so the theory goes, they are just good enough on offense to outscore any opponent.

In reality, all great teams are fairly complete, meaning that they are good in all phases of the game. You can’t really be a great team if you have a bad defense. What apparently fools everybody is the fact that football is a game with no set pacing. A baseball game is nine innings, or twenty-seven outs if you prefer. Golf is eighteen holes. A set in tennis is six games. Games like football and basketball are different. A football game can range, at the extremes, from something like seven possessions (this year, Navy v. Notre Dame) to as many as seventeen or eighteen. The typical range is more like 9-10 for a low-possession game, and perhaps fifteen for a high-possession game. But, as with basketball, certain teams tend to play high-possession games, and certain teams tend to play low-possession games. Teams that play high-possession games generally feature hurry-up offenses, or pass-happy offenses, or defenses that prefer to gamble for stops rather than playing “bend but don’t break”. Teams that play-low possession games will be teams that run the ball a lot, or play conservative defense that seeks to avoid giving up big plays at the expense of allowing lots of first downs. As should be somewhat obvious, teams that play high-possession games tend to score more points, and they allow more points, all else being equal. For some reason, we collectively seem to appreciate this in basketball, and we don’t necessarily consider low scoring teams to be “bad” on offense. We look to efficiency rankings instead.

Football analysis is catching up, but nobody seems to be taking notice. The stats I will be quoting are from Football Outsiders (very good site if you’ve never seen it).  This site ranks offenses and defenses as units, based on some advanced per-possession stats that attempt to adjust for quality of opponent. This is obviously an imperfect process, but in my opinion it provides much better information than simply saying that, because a team averages 35.6 points per game, they are “good” on offense.

Oregon has long had a reputation as a high-flying offense and a poor defense. I think it is time to challenge that assumption. Offensively, they’ve been good, no question. Since 2007, their offensive ranks have been 7th, 13th, 11th, 11th, 5th, 2nd, 6th, 1st, 13th, and 18th. This year, their 18th rank is their worst on offense in a decade. That’s pretty good. But what about defense? Since 2007, they have been 19th, 42nd, 22nd, 5th, 9th, 4th, 29th, 28th, 84th, and 126th. Raise your hand if you are surprised, particularly about the stretch for 2010 to 2012 (5th, 9th, and 4th). The 2010 national title game was billed as two great offenses against two bad defenses (Auburn and Oregon), yet somehow, those two defenses held the great offenses to some of their lowest point totals all season (22 to 19). Turns out, when analyzed properly, both were great defenses as well (that just so happened to be playing with extreme, hurry up offenses, so they played many high scoring games).

I still consider the absence of a playoff in 2012 to be a travesty. 2012 Oregon vs. 2012 Alabama would have been a great game, and we needed to see it. If only somebody could have beaten Notre Dame during the regular season…  Oh well.

In any event, Oregon’s national-title-contender status from 2010 to 2014 was based upon great offense AND great defense. Last year they still managed to be 9-4, a pretty good year, with the 84th defense. But this year, with a truly terrible defense, they are 4-8, despite still having a great offense.

And that is normal. Many teams follow that formula. For example, 2013 Indiana (8th offense, 105th defense, 5-7 record), 2012 Baylor (5th offense, 94th defense 8-5 record), 2010 Michigan (8th offense, 107th defense, 7-6), 2009 Stanford (4th offense, 104th defense, 8-5 record). Another prominent team that had this reputation was West Virginia under Rich Rodriguez. As a 2007 national title contender that lost in an upset to Pitt to drop out of the title game, then routed Oklahoma in the Fiesta Bowl, they were 3rd on offense and 9th on defense. Not quite what most people thought.

The bottom line is that you won’t be a great team without being at least good on defense. There may be an exception or two (I haven’t researched every team from all time), but the general rule is pretty clear: if you are an elite offense and a below-average defense, you will be .500 or maybe a little better. 8-5 or 9-4 is about the best you can possibly do, and most do worse. Anybody winning 11 or 12 games has a good defense. Don’t be confused if a team like that sometimes gives up a lot of points. Maybe they are playing against a great offense, and/or defending more possessions than most other teams. If they are 12-2, its virtually guaranteed they’ve got a strong defense. Don’t believe the myth.

Baseball Notes: The WAR on Robbie Ray

baseball notes

There are a few things we know with reasonable certainty about Robbie Ray. He was born on October 1, 1991 just south of Nashville in Brentwood, Tennessee. In 2010, the Washington Nationals drafted him in the twelfth round of the amateur draft. The Nationals traded him, along with two other players, to the Detroit Tigers in 2013 in exchange for Doug Fister. A year later, the Tigers traded him to the Arizona Diamondbacks as part of a three-team trade that netted the Tigers Shane Green and the New York Yankees Didi Gregorius. So far, Ray has seen major-league action as a starting pitcher with the Tigers and Diamondbacks. He showed promise in his first three appearances (two starts and an inning of relief), for Detroit. He showed less promise in his remaining six appearances– four starts and two relief innings– for that team. Things have ticked back up for Ray since his arrival in the desert, however.

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Most baseball fans likely have some familiarity with the player-valuation concept of wins above replacement player, usually labeled WAR. What many fans may not realize, however, is that there actually are three different versions of the WAR statistic. The goal of each version is the same: to determine a comprehensive valuation of an individual baseball player. Each takes slightly different paths to reach that comprehensive valuation, but they typically reach similar conclusions about a given player, such that it’s common to see or hear a player’s WAR cited without specific reference to the particular version utilized.

For example, the three versions– Baseball-Reference’s WAR (“rWAR”), FanGraphs’ WAR (“fWAR”), and Baseball Prospectus’ WARP (“WARP”)– all agree that Mike Trout had a great 2016. He finished the season with 10.6 rWAR, 9.4 fWAR, and 8.7 WARP, good for first, first, and second by each metric, respectively. For another example, they also agree about Trout’s former MVP nemesis, Miguel Cabrera: 4.9 rWAR, 4.9 fWAR, 3.9 WARP. (In my anecdotal experience, WARP tends to run a little lower than rWAR and fWAR for all players.)

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While the WAR varietals typically and generally concur, that isn’t always the case. Pitchers can be particularly susceptible to this variance, because the measurement of pitching performance is one of the areas in which the three metrics are most different. Continue reading