Relievers and Consistency

Are relief pitchers more volatile than starters? While that seems to be the case, relievers also get evaluated in much smaller sample sizes.

Statistics usually get expressed in terms of seasons. That works great for starters, but not so well for relievers. Starters throw approximately three times as many innings as bullpen pitchers in a typical season, which gives them far more time to work through their struggles.

For this reason, we compared starters in one-third of a season timeframes to relievers in full seasons. This evens the playing field to help determine which group maintains more consistency.
We identified all relief pitchers that pitched 150 innings or more from 2008 through 2010 and posted an earned run average between 3.00 and 3.50. The starters had to pitch 150 or more innings in 2010 alone with an ERA in that same range. Nineteen relievers and 17 starters met the criteria.

The starters group included CC Sabathia, Cliff Lee, Chris Carpenter and Tim Lincecum. The relievers had pitchers like Francisco Cordero, Huston Street, Brian Fuentes and Jonathan Broxton. The two groups accumulated a comparable number of innings: the bullpen group totaled 3510.2 innings versus 3602.1 for the starters. Both groups had an identical ERA of 3.25.

Based on inspection, the starters appeared slightly more volatile. None of the relievers posted an ERA over 5.00 in any of their annual timeframes. However, two starters did so in their two-month periods: Max Scherzer (6.42) in April/May and Jon Garland (5.16) in June/July. The starters failed to post a single ERA below 2.00, while the bullpen group had three: Jeremy Affeldt (1.73) and Ryan Franklin (1.92) in 2009 and Chris Perez (1.71) in 2010.

The best way to measure consistency is through standard deviation, which simply measures how much figures differ from the average. The standard deviation for the starters (.635) was slightly lower than the relievers (.677). Basically, there was very little difference in consistency between these groups.

The starters also had one huge advantage. While the relievers had an entire offseason in between their seasons, the starters flowed directly from one two-month period into the next. In several cases, the relievers changed teams and/or leagues during the offseason, making it even harder to sustain consistent performance.

Before making any conclusions, more data should be examined beyond this rather small study. It would also be valuable to examine statistics like OPS allowed, since ERA does not account for how relievers pitch with inherited runners.

Nonetheless, it appears that what some perceive as lack of consistency has more to do with the limited number of innings relievers pitch per season.

The Truth about Strikeouts

Managers hate when hitters strike out. The mainstream media often criticizes high-strikeout players like Mark Reynolds, much more than it should.

When it comes to winning and losing, strikeouts by hitters aren’t much more costly than other types of outs. While pitchers’ strikeouts have a major effect on run scoring, the same doesn’t hold true for hitters.

This insight comes from research using an advanced statistical technique called regression analysis. Without getting into the details, regression analysis determines how well statistics correlate with each other. Pitchers’ strikeouts have a much greater correlation with run prevention than hitters’ strikeouts have on run scoring.

How can this be? In general, hitters who strike out a lot also hit home runs and draw walks. On the other hand, strikeout pitchers limit offense better on average than pitchers who miss bats less often. They are also less dependent on their defense to make plays behind them.

Whether in arbitration or free agency, baseball agents can emphasize the value of high strikeout pitchers. And if you represent a high-strikeout batter, exhibits with this information provide hard evidence in his favor.

Opportunity and Statistics

Most sports statistics – especially the ones that get attention in the mainstream media – are opportunity based. Other metrics filter out opportunity, and they carry tremendous comparative value.

Many still fixate on per game numbers, and they don’t begin to tell the story for players like DeJuan Blair. His 7.8 points per game and 6.4 rebounds per game in 2009-10 look pedestrian. However, Blair posted these numbers in limited opportunities – playing just 18.2 minutes per contest.

Rebounds per 48 minutes is not impacted by how much players see action. Among NBA players with at least 750 minutes played, Blair ranked sixth in rebounds per 48 minutes (16.9). He topped all NBA players in offensive rebounds per 48 minutes (6.43), and remember that he was a 20-year-old rookie!

Even the offensive rebounds per 48 minutes statistic gets impacted by opportunity. Some teams play at a faster pace than others, and some miss more shots. Their players have more opportunities to grab offensive boards. The Spurs played at a slower pace than most teams and had the NBA’s sixth-highest shooting percentage. So these factors hurt Blair, yet he still out-rebounded everybody at the offensive end.

The best metric to show Blair’s rebounding excellence is rebound rate, John Hollinger’s measurement for the percentage of missed shots that a player rebounds when he’s on the court. Blair had a 16.0 offensive rebound rate last season. To put that in perspective, NBA teams grab 26-27 percent of available offensive boards on average. The Golden State Warriors had an offensive rebound rate of 20.9. Blair fell just 4.9 short of that figure, by himself.

While playing his final season at Pittsburgh, Blair put up unbelievable stats in this category. Despite playing in the rugged Big East, his 23.6 offensive rebound rate topped the nation’s next closest player by 5.0. Blair even surpassed the team figure for six Division I colleges.

So how did a player – who can out-rebound an entire team – last until the 37th pick of the 2009 NBA Draft? It’s hard to say. Blair’s 2008-09 rebounds per game figure (12.3) looked good but unspectacular, which may have been a factor. Of course, he played only 27.3 minutes per game on a very slow-paced team. Only adjusting his numbers for opportunity made Blair stand out.

Caution: Falling Offense

Remember 1992? That was the last time National League offense had gone lower than the current level of 4.36 runs per game. The same goes for the American League, which has seen an even sharper scoring drop-off since last season. AL teams averaged 4.82 runs per game in 2009. That figure had plunged to 4.45 through September 14. The NL had a more gradual decline from 4.43 runs per game last year to 4.36.

This presents a challenge for agents with arbitration-eligible and free agent position players this offseason. Clubs will no doubt pull out comparables from recent seasons when the run context was substantially higher.

Fortunately, there is a solution. Agents can adjust for the decreased offense in the same way economists do so for inflation. The Sports Resource has built a statistical model that adjusts for run context, which helps your position player clients when scoring drops.

You can even turn the scoring trend into a positive for hitters: some of this season’s individual achievements will stand out even more at contract time. For example, should Jose Bautista reach 50 home runs, he will match a feat last accomplished in 1990. Look for another post on this topic in the weeks ahead.

Who is Today’s Jose Cruz?

I drove past the Astrodome last week, seeing the old stadium for the first time. Now dwarfed by the adjacent Reliant Stadium, it brought back memories of 1-0 victories thrown by great Houston pitchers like Nolan Ryan and J.R. Richard.

Through most of its history, the Astrodome was an awful place to hit a baseball. Jose Cruz had the misfortune to play there in the 1970s and 80s. Had Cruz played in Fenway Park or Wrigley Field back then, he may be remembered as one of his era’s greatest hitters.

Cruz hit 59 career home runs in his home parks and 106 in road games. Although he started with Cardinals and ended up with the Yankees, Cruz had 83 percent of his career plate appearances for the Astros.

During his peak from 1976 to 1986 – when he played exclusively for the Astros – Cruz had a 128 OPS+ according to BaseballReference.com. Since this metric adjusts for both the league average and a player’s ballpark, the Astrodome’s negative impact gets stripped away. Cruz ranked 24th in OPS+ among players with 2500 plate appearances from 1976-86, finishing in a group of more heralded players like Dale Murphy (129 OPS+), Cal Ripken Jr. (129), Kirk Gibson (128), and Dave Parker (128).

In that same timeframe, Cruz hit 100 homers and stole 250 bases. Only Andre Dawson, Rickey Henderson, and Davey Lopes joined him at those levels. Cruz reached base 2412 times, more than all but five other Major Leaguers from 1976-86.

While there are no stadiums like the Astrodome today, Safeco Field and PETCO Park have a comparable impact on offense. Although we now have tools that few knew about during Cruz’s playing days to adjust for run context, they still get limited attention.

Ballparks have a huge impact on statistics, yet many fail to take this into account in solving the value puzzle. Examining park effects is vital for not only showing a player’s true performance level, but where his career is headed.

Hot Topics from the SABR Convention

The Society for American Baseball Research Convention, held earlier this month in Atlanta, had something for every diehard fan. I’ll focus this post on topics of greatest interest to baseball agents.

Vince Gennaro, a consultant for Major League teams and author of the book “Diamond Dollars”, gave a fascinating talk on the economics behind midseason trades. He pointed out that a player’s true value is different for every team. For example, Cliff Lee had a much greater value to the Rangers than the Mariners this season.

Gennaro described how much team revenues get impacted by winning. Just reaching the postseason has a $25-to-$50 million benefit to teams. And it has a multi-year effect for up to five seasons!

It occurred to me that if the playoff races stay close, a number of arbitration-eligible players could make the difference between their club earning a playoff spot and missing out. That would carry some weight this offseason!

The New Technologies and Baseball panel was a serious eye opener. Based on the capabilities of the data becoming available, I wouldn’t be surprised if terms like “launch angle” become common in the next five years. It’s now possible to analyze the flight of both a pitched and batted baseball. Want to know which batters hit the ball the hardest? It’s all there. Among numerous other applications, this information could be used to determine whether a hitter is truly in a slump or hitting the ball just as well but experiencing bad luck.

Physicist Alan Nathan used PITCHf/x data to show the brilliance of Mariano Rivera. But he debunked the theory that his pitches have “late break”. This is actually an illusion caused by the fact that one of Rivera's cutters breaks about five inches more than his other cutter.

J.C. Bradbury, author of “The Baseball Economist”, gave a great presentation on pitch counts and days of rest. He showed data revealing that – contrary to popular opinion – pitch counts have remained stable since 1988. But minimum pitch counts by starters have actually increased, possibly as a result of managers looking to ease the workload on their bullpens.

Bradbury also found that there was little difference in performance by pitchers working on just three days rest versus four. This always becomes a hot topic in the postseason.

In general, the conference demonstrated how much research is out there to help agents build value for their players.

Keeping it Simple

I have been reading “The House Advantage” by Jeff Ma. The book explains the importance of incorporating data into the decision process. Ma uses examples from his background in sports and as a member of the MIT blackjack team; he was the central character in the best-selling book “Bringing Down the House”.

In one chapter, Ma writes about the approach he takes when consulting with businesses, whether sports-related or not. He summarizes it without using any complex statistical terms, but as taking information from the past to make decisions about the future. Whether in sports or business, that process creates an edge.

This applies to most sports decisions, whether for free agency or the draft. What trends are in a player’s past that bode well for their future? That track record may be long and consistent with plenty of data, as with Albert Pujols or Tim Duncan. Or it could be brief, such as the case of a young lefty relief specialist or a college freshman entering the NBA Draft. Generally speaking, the more the data, the easier it is to project performance going forward.

The quality of the data is also important. When projecting Ubaldo Jimenez’s performance for the season’s final two months, his impressive won-lost record through July is not all that helpful. His ERA is more valuable, but still not the best predictor. The best projections come from examining Jimenez’s walks and home runs allowed, the percentage of batters he has struck out, and factors like left on base percentage and batting average on balls in play. Performance in previous seasons matters too, as that is all part of his track record.