Archive for Research

We hear about position scarcity all the time, but category scarcity also plays a role in valuing players. In 2000, 47 players hit at least 30 HR (hmm, wonder why?) as compared to just 18 players in 2010. Mark Reynolds hit 32 HR last year and tied for 10th in baseball. Many fantasy owners continued to start Reynolds every day despite his sub-Mendoza .198 average because his power was so valuable. Had Reynolds hit 32 HR with a .198 average back in 2000, he would have been riding the digital pine. Power wasn’t at a premium back then.

And that’s category scarcity in a nutshell. In fact, position scarcity is really just a function of category scarcity. Shortstop is only considered shallow because there are so few players who can contribute across the board. A quick look at any shortstop rankings shows how rapidly talent plummets at the position.

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ERA can be a weird thing at times. I love it, but it doesn’t always reveal the full story. Jason Hammel is the perfect subject. After six years in the Rays minor league system, and three bad stints with the Rays Major League club, he found himself looking up at a logjam of starting pitchers in Tampa Bay. The Rays traded him to the Rockies after the 2008 season in exchange for Aneury Rodriguez.

With the trade to Colorado, Hammel was given a great opportunity to start in the Majors for a full season. Since his arrival in Colorado two seasons ago, Hammel has been nothing but consistent. Take a look at his stats:

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This post originally appeared on Yankeeist.

It’s no secret that Alex Rodriguez produced the lowest full-season wOBA of his career in 2010 — his .363 mark was fueled by career-lows in batting average (.270), on-base percentage (.341) and the second-lowest full-season SLG of his career (.506). That these numbers were not only dramatically off from his superb 2009 (.286/.402/.532; .405 wOBA) but his majestic career triple slash (.303/.387/.571) suggests to me that he should be due for a reasonable bounceback. While it’s not impossible Alex has reached an irreversible decline, he’s been too historically good for me to be willing to write him off just yet. I won’t go so far as to proclaim that the Yankees are going to be getting .400-plus-wOBA A-Rod back, but as I’ve noted on at least one occasion this offseason, all A-Rod needs to do is exercise just a tad more patience and a wOBA in the .380s should be more than doable.

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This was originally posted on WahooBlues.com

When the Baseball Writers Association of America announced Wednesday that Roberto Alomar and Bert Blyleven had been elected to the Baseball Hall of Fame, two worthy inductees who had waited too long were granted entrance to Cooperstown. But to judge the voting process solely by the selections of two worthy candidates would be to ignore the massive problems with the way the BBWAA does business.

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2010 marks the end of the “aught” decade for Major League Baseball.  I thought I would take the opportunity to analyze the last 10 years by visualizing team data.  I used Tableau Public to create the visualization and pulled team data from ESPN.com (on-field statistics) and USA Today (team payroll).

The data is visualized through three dashboards.  The first visualizes the relationship between run differential (RunDiff) and OPS differential (OPSDiff) as well as the cost per win for teams.  The second visualization is in table form and can be sorted and filtered along a number of dimensions.  The final visualization looks at expected wins and actual wins.

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Lee has a tremendous variety of movement in his pitches. He has three pitches that tail away from righties (fourseam, twoseam, changeup) and a nasty curveball with a ton of movement. For most pitchers this would be plenty; but Cliff Lee is not like most pitchers. He also packs a cutter with as much horizontal movement as some sliders.

We can see this with the following graph, which is from the catcher’s perspective (same with all following graphs):

CU=curveball, FC=cutter, FF=fourseam, FT=twoseam, CH=changeup. The black box represents the strikezone and has the average pitch locations for each pitch.

Looking at a pitcher’s entire repertoire like this is useful, but it can be more interesting to look at pitches individually when it comes to pitchers like Lee.

Against righties his location is pretty varied with the fourseam. He mainly locates the pitch middle-away, but often goes up and in too. Against lefties, he consistently pounds the outer half.

Against righties he primarily throws the twoseam pitch up and away, which explains why he has a high flyball rate on a pitch typically associated with groundballs. Against lefties the pitch is pretty much thrown low and over the middle of the plate.

Against righties the pitch is a real weapon; the cutter results in many whiffs and a solid amount of groundballs. Against lefties the pitch isn’t as remarkable, but still solid. His location against lefties with the cutter is very similar to his location with his fourseamer against lefties.

His location against righties and lefties is pretty much the same, though he does backdoor the pitch occasionally to righties. He pretty much only throws his curve late in counts for strikeouts.

Only one graph here because he only threw 20 changeups to lefties the entire year, so I’m just going to ignore those. According to Fangraphs pitch run values, his changeup was his most effective pitch this year. And you can see why; he was great and locating the pitch down and away.

*all data and tables are from Joe Lefkowitz’ site.

*This article was originally posted on www.pendingpinstripes.net

I am a big fan of graphs and baseball. Fangraphs made me excited because putting complex data into reasonably easy to understand graphs helps open up sabermetrics to more fans. I’m a big fan of statistical analysis, but after a while, a table full of numbers just starts running together and stops making sense. That’s what makes graphs such an effective tool.

I’ve dabbled in graphs myself. When people were creating the WAR graphs to compare hall of famers, I made a sample graph showing cumulative WAR by age on Tom Tango’s Book Blog:

(click for a larger image)

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For the first time ever in the divisional era, 1 division has managed to run the table against the other two. Barring the Royals taking 3 of 4 from Tampa over the weekend, there won’t be a single team in the American League who will have put together a winning record against the AL East.

Since 2007, only 8 teams in the American league have been able to put together a winning record against the AL East, (LAA 3 times, OAK twice, TEX, DET and SEA once). This season, as things currently stand, the Central and West divisions have combined to go 149 – 198 (.429%) winning percentage against the AL East, a record comparable to the Cleveland Indians or the Washington Nationals.

What’s worse, is that the AL East will end again with the team that has either the worst, or second worst record in the American League (assuming Buck Showalter doesn’t phone it in this weekend), but looking deeper into their divisional performance, how bad exactly is the AL East’s punching bag?

(Record, Winning Percentage, % Diff from total)
2010 Orioles: Non-AL East Record (39-47*, .453, +.054), 4 games at home v. Detroit left
2009 Orioles: Non-AL East Record (40-50, .444, +.049)
2008 Orioles: Non-AL East Record (46-43, .516, +.095)

AVERAGE % Diff = +.066, or 10.7 Wins

And the 4th place Jays?

2010 Blue Jays: Non-AL East Record (43-43*, .500, -.019), 4 games in Minnesota left
2009 Blue Jays: Non-AL East Record (49-41, .544, +.081)
2008 Blue Jays: Non-AL East Record (49-41, .544, +.013)

AVERAGE % Diff = +.025, or 4 Wins

This means, on average, even the worst that the AL East has to offer, averages almost 5% points higher against non-divisional foes, or roughly 7 wins better across a 162 game season. How much are those 7 wins worth? If you’re Tampa, obviously zero since the only way to fill the ballpark there is to give away 20,000 tickets, but to a team like Seattle or Toronto (as highlighted above), the 2009 Jays managed 75 wins at 23,162 fans a game, whereas the 2008 version of 86 wins averaged 29,626. Obviously its a very rudimentary look at attendance and I’m ignoring plenty of factors, but the fact is and has always been that outside of Tampa Bay, people will inevitably jump on the bandwagons and go to see winning teams win ballgames. The point I’d like to be making here is the financial impact of regional divisional slotting, and if you take the leap with me, obviously the analysis will have a more profound impact.

For comparison purposes, lets look at the AL CENTRAL 4th and 5th place finishers

2010 Kansas City: Non-AL Central Record (36-50*, .418, +.007), 4 games at home v. Tampa left
2009 Kansas City: Non-AL Central Record (33-57, .367, -.034)
2008 Detroit: Non-AL Central Record (47-43*, .522, +.065), 13-5 in interleague games

2010 Cleveland: Non-AL Central Record (33-55, .389, -.039)
2009 Cleveland: Non-AL Central Record (33-55, .389, -.012)
2008 Kansas City: Non-AL Central Record (44-46*, .489, +.025). 13-5 in interleague games

AVERAGE % Diff = .002, or +.4 Wins

The 2008 figures are both skewed in the positive directions due to AL Central success during interleague play, getting to play the even weaker NL West in the majority of their IL games, and zero games against that division’s winner (84-78 Dodgers), so discounting those games, the result is:

AVERAGE % Diff = -.015 or -2.7 Wins

Does the AL East really cost a team 7-10 Wins a season like it has the Orioles? Probably not, there are plenty of other factors that go into a teams eventual W-L, but traditional Strength of Schedule metrics that value the winning % of your opponents, or Runs Scored/Runs Allowed Differentials often don’t capture the simple facts that your record and team stats will be positively influenced by facing the Royals 18 times a season. Who knows, maybe the 2010 Blue Jays, who lead the majors in slugging and home runs, could’ve competed this season in another division where its young pitching staff wasn’t exposed to the three highest scoring teams in the league (NY Boston Tampa) for 54 games? Under this current alignment, we can never be too sure.

On the positive, at least the divisions regionally make sense in their current form, unlike football. I’m sorry, putting Miami in the AFC EAST, Baltimore in the AFC NORTH, and Indianapolis is in the AFC SOUTH makes about as much sense as the BCS national championship formula.

Over the last couple of weeks there has been a lot of debate about the value of home field advantage in baseball. The discussion was crystallized most recently when the Yankees rested key bullpen members in their first place showdown against the Rays, but it has really be going on since the advent of the Wild Card.

Yesterday, we wondered about the value of finishing first from an accomplishment perspective, but ultimately, that is a very intangible way of looking at the question. At SI.com, Joe Seehan looked at home field advantage from a competitive standpoint and came to the conclusion that it really wasn’t an advantage at all. According to Sheehan’s research, the number one seed has advanced to the World Series in only eight of 24 chances since 1998, when the current playoff format was established. What’s more, over that span, the home team has only gone 45-39 in all post season series, according to Sheehan. In other words, there really isn’t a home field advantage in baseball during the postseason.

There is at least one more vantage point from which to consider this question, and it could very well be the most important: economics.

In the post season, gate revenue (i.e., attendance) is divided between the players and hosting team using the following format:

• Players: 60% of gate receipts from first three games of LDS and first four games of LCS and World Series; no contribution from other games.
• Home team: 40%* of gate receipts from first three games of LDS and first four games of LCS and World Series; 100% of gate receipts from all other games.

*A small percentage (approximately 1.5%) of LDS gate receipts goes to the umpires, while 15% of LCS and World Series gate receipts go to MLB.

On the face of it, there seems to be an economic advantage to having home field. But, is it real, and if so, how significant is it?

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We’ve all heard a broadcaster comment on the impending doom of a leadoff walk and yet they fail to seem to apply the same sort of fateful outcome for a single. I thought it would be interesting to find the outcomes of each of the ways a player can leadoff an inning by getting on first base and see if it affects whether or not the runner goes on to score. I took the retrosheet data sine 1952 (but not including this year) that I have as a MySQL database and created a quick python script to determine these results. I took it further and examined if the breakdown were any different in late game situations, as I’m always hearing “You never want to walk the leadoff batter but especially late in close ball games”. I was also curious if even in general more solitary runs get manufactured once a leadoff runner gets on base in late game situations.

Total times batter lead off an inning by getting to first: 508312
Total times runner scored: 192150

So a leadoff batter who starts on first base scores 37.80% percent of the time, here is the breakdown via the means they get aboard

Any inning

Single      325455 Scored 122662   37.69%
Walk        150570 Scored  57189   37.98%
HBP          11865 Scored   4600   38.77%
Error        19260 Scored   7270   37.74%
Strikeout     1007 Scored    375   37.24%
Catcher's Int. 155 Scored     54   34.84%
Totals      508312 Scored 192150   37.80%

So it appears as though it’s not much of a statistically significant difference between the walk and the single. The HBP numbers seems to be a bit of an outlier, I’m wondering if that is just sample size or if such an outcome rattles the pitcher to the point of that much more runs being produced.

Lets now examine the breakdown based upon the stage of the game.

6th inning or earlier

Single      217421 Scored  83243 38.29%
Walk        100587 Scored  38798 38.57%
HBP           7879 Scored   3070 38.96%
Error        12778 Scored   4880 38.19%
Strikeout      645 Scored    244 37.83%
Catcher's Int. 107 Scored     36 33.64%
Totals      339417 Scored 130271 38.38%

7th inning or later

Single     108034 Scored 39419 36.49%
Walk        49983 Scored 18391 36.79%
HBP          3986 Scored  1530 38.38%
Error        6482 Scored  2390 36.97%
Strikeout     362 Scored   131 36.19%
Catcher's Int. 48 Scored    18 37.50%
Totals     168895 Scored 61879 36.64%

Interesting how 1.74% more leadoff runners reaching first score in the earlier innings.  Is this a comment on the failure of manufacturing runs or pitching being different in the later stages of the game?  Perhaps a deeper look based upon “close game situations” is in order for that.