Felix Hernandez Heads AL Run Prevention Leaders

Mariners right hander Felix Hernandez is good at preventing runs by any measure
(Photo Credit: Marinersblog.MLBblogscom)

With the season now about half complete, it's a good time to update the Tiger Tales sabermetric pitching Leaders. 

There is no surefire way to determine the best pitchers in the league, but a pitcher's job is to prevent runs.  So, it's useful to estimate how many runs pitchers saved their teams compared to an average pitcher.  In the past, I have explored four different ways to do this:    

• Pitching Runs -  Runs Saved Above Average based on innings and runs allowed. 

• Adjusted Pitching Runs - Pitching Runs minus estimated Runs allowed by fielders. 

• Base Runs -  Runs Saved Above Average based on batters faced and hits, walks, total bases and home runs allowed.

• FIP Runs - Runs Saved Above Average based on innings, bases on balls, hit batsmen and home runs allowed and strikeouts.

These measured are discussed in more detail in an earlier post.  After computing each measure, I then take the average of the four. 

The current American League leaders are listed in Table 1 below.  Mariners ace right hander Felix Hernandez leads the league in Base Runs (31) and FIP Runs (31), is second in FIP Runs and fifth in Adjusted Pitching Runs.  That gives him an average of 26 runs prevented compared to an average pitcher. 

The leading Tiger is Anibal Sanchez with an average of 14, good for seventh in the league.  He is tied for fourth in Base Runs (17). but only 15th in Pitching Runs.  Other Tigers in the top 20 are Rick Porcello (11 runs prevented) and Max Scherzer (10).  Drew Smyly checks in at +3 and Justin Verlander is a disappointing -7 worse than league average.  

Table 1: AL Run Prevention Leaders as of July 2, 2014

Pitcher	Team	IP	Pitching Runs	Adjusted Pitching Runs	Base Runs	FIP Runs	Average
Felix Hernandez	SEA	128.1	24	19	31	31	26
Masahiro Tanaka	NYY	115.2	25	29	17	18	22
Garrett Richards	LAA	109.0	18	18	23	17	19
Yu Darvish	TEX	104.1	17	23	13	21	18
Chris Sale*	CHW	78.1	16	20	18	15	17
Dallas Keuchel*	HOU	103.2	15	18	15	15	16
Anibal Sanchez	DET	82.0	10	14	17	15	14
Scott Kazmir*	OAK	103.1	17	14	15	9	14
Mark Buehrle*	TOR	115.1	19	21	7	8	14
Corey Kluber	CLE	117.1	11	14	8	20	13
Jon Lester*	BOS	114.0	7	10	9	20	11
Rick Porcello	DET	106.2	11	17	10	7	11
Phil Hughes	MIN	103.0	7	10	7	19	11
Sonny Gray	OAK	104.0	10	8	12	10	10
Max Scherzer	DET	111.1	8	13	3	17	10
David Price*	TBR	131.0	4	4	9	18	9
Chris Archer	TBR	100.0	5	6	11	13	9
Danny Duffy*	KCR	72.2	11	9	12	2	9
Jose Quintana*	CHW	104.2	1	7	6	16	8

Data source: Baseball-Reference.com

Do Walk-Off Wins Create Momentum?

Last night, the Tigers were down 4-1 in the bottom of the ninth and many of you had probably gone to bed for the night or were doing something else thinking the game had been lost.  After all, there wasn't much chance against untouchable Athletics closer Sean Doolittle.  I was only half listening myself at that time, instead looking up the results of one of my fantasy teams.

I knew the Tigers were threatening though and Doolittle wasn't his usual dominating self having just walked slumping outfielder Austin Jackson (just Doolittle's second walk of the season) to load the bases with one out.  So, I looked up at the television screen to see Rajai Davis at the plate and Ian Kinsler on deck.  It never occurred to me that Davis might be the hero and I was mostly waiting to see what Kinsler would do. Then Davis shocked us all by connecting on a hanging slider for a line drive home run to left field, a walk-off grand slam.  

It was the most improbable and exhilarating victory for the Tigers so far this year, but what does it mean for the future?  Does the walk-off win make it more likely that the Tigers will win tonight?

It is widely believed that a walk-off win or sudden victory creates a boost for a team that carries over to the next game more often than not. I wanted to see if this was true (I actually did this study last year after the Red Sox walked off the Tigers in Game one of the American League Championship Series).  So, I went to the retrosheet database and found all walk-off wins between 1995 and 2012. There were 3,769 of these sudden victories during the period which comes out to about seven per team for a season. My goal was to see if walk off winners had a tendency win the next game after their walk offs.

Twenty-eight walk offs fell out of the sample because they occurred in the final game of the season and thus were not followed by another regular season game. That gave me 3,741 games with which to work. I discovered that teams won 52.6% of the games immediately following walk off wins. That's more than 50% so at first glance it seems that there is a bit of a tendency for teams to win games following walk offs.

Before jumping to conclusions though, there are a couple of important factors to consider. First, walk off wins only happen at home so chances are (85% of the time to be exact) that the next game would also be at home. Since teams win 53.9% of their home games, you would expect them to have a good winning percentage in games after walk offs even without a carryover effect. Also, teams with good winning percentages tend to have more walk off wins. For both of these reasons, one would expect a win the next game after a walk off more than 50% of the time even if walk-off wins had no influence on future games.

I calculated the expected winning percentage in games after sudden victories as follows: For each walk off, I calculated the winning percentage of the specific team for that year and site. For example, the 2012 Tigers won 61.7% of their home games. Thus, they would have a probability of .617 of winning a home game the day after a walk off assuming no carryover effect. I did this for each of the 3,741 walk offs and then calculated the average probability to be .528. This means, that assuming no carryover effect, we would expect 52.8% of the games after walk offs to have resulted in wins.

Since the expected winning percentage (52.8%) for games after walk offs was almost exactly the same as the actual winning percentage (52.6%), I can conclude that, in general, a walk-off win has no effect over the result of the following game. As cautious as I am about accepting intangibles, this result is still surprising to me. I was not expecting a dramatic effect but I thought that sudden victories would have a small influence over ensuing games.

Another question is whether walk-off losses create negative momentum.  This is, of course, relevant to the Tigers-Athletics scenario since the walk-off winner is facing a a walk-off loser in game two of the series tonight.

As with walk off wins, there are there are two factors to consider. First, walk-off losses happen on the road and are followed by road games 86% of the time. Since teams win just 46.1% of their road games, you would expect them to have a low winning percentage in games after sudden losses even without a carryover effect. Also, teams with low winning percentages tend to have more walk off losses. For both of these reasons, one would expect a win in the next game after a walk off loss less than 50% of the time even if walk-off losses had no influence.

The expected winning percentage in games after sudden losses is calculated the same way as for walk-off wins.  It turns out that a team would be expected to win 45.7% of the time after a walk-off loss if there is no carryover effect.  Since, this is almost the same as he actual winning percentage of 46.4%, I can conclude that,  in general, a walk off loss has no affect on the result of the following game. 

Based on these analyses, the Tigers thrilling victory should not affect their performance in game two any more than any other result. Tigers former manager Jim Leyland's theory was that "Momentum is only as good as the next game's starting pitcher".  So, let's hope that emerging stalwart Rick Porcello is on his game tonight versus the Athletics.  

How is Alex Avila's Defense in 2014?

Alex Avila has excelled at stopping the running game and pitch blocking in 2014
(Photo credit: TheMajors.net)

One of the hardest parts of the game to quantify is catcher defense.  It is believed by many baseball insiders that handling of pitchers is the most important defensive skill of any catcher.  By pitcher handling, I mean studying opposing batters, game calling, understanding pitcher abilities and tendencies, helping pitchers maintain focus and other duties unique to the catching position.  These things are difficult to measure because we do not know how much of good pitching is due to the pitcher versus the catcher.  Much of pitcher management is still a mystery to statistical analysts, but there are some things than can quantified.

The algorithm I have used to evaluate catchers is complex and will not be described in detail here, but the methodology can be found in an earlier article.  I do want to give credit to others such as Sean Smith, Justin Inaz, Matt Klaasen and Mike Rogers who inspired me with similar work in the past.  The system evaluates catchers based on what we can most easily measure - controlling the running game, pitch blocking and avoiding errors.  Thanks to analysts such as Mike Fast, Max Marchi and Harry Pavlidis and Dan Brooks, I can now add a new component to my formula - pitch framing or receiving.

I'll use Detroit Tigers backstop Alex Avila as an illustration.  Based on innings caught, stolen bases attempted, runners caught stealing and league caught stealing rate, it is estimated that Avila has saved the Tigers about five runs (4.5) compared to an average catcher.  This is tied for best in the majors with Yadier Molina of the Cardinals.

Similarly, passed ball and wild pitch rates suggest that Avila has saved the Tigers an estimated three runs (3.2) with pitch blocking.  This is second only to Jonathan Lucroy of the Brewers who has 4 runs saved. better   Avila has not cost the Tigers any runs on throwing errors (0.2) or fielding errors (-0.2). 

Finally, I take the pitch framing data from Baseball Prospectus pitch receiving data from.  According to Pitch f/x data, there should have been 1,568 strikes called with Avila behind the plate.  There have actually been 1,543 strikes, so he has cost the Tigers 25 strikes with translates into an estimated four runs (-3.7).  

The five elements listed above (stopping the running game, pitch blocking, avoiding throwing errors, avoiding fielding errors and pitch receiving) are combined to arrive at total runs saved.  Avila's numbers sum to about four runs (4.1) indicating that he has saved the Tigers an estimated four runs overall with his catching. 

So, Avila has been good at at stopping the running game and pitch blocking, but not so good at pitch receiving.  It's interesting to note that the opposite was true last year when he was -6 stopping the running game, -2 blocking pitches and +9 receiving pitchers. 

Like other defensive algorithms, this system should be taken with a grain of salt.  First, it does not address important pitcher management skills.  Moreover, pitch receiving measurement is a work in progress.  There is evidence that these numbers tend to stay relatively consistent from year to year though indicating that they probably describe real skills to some extent.

Table 1 below shows that Yankees receiver Brian McCann is the major league leader with 17.7 runs saved in 2014.  He has been especially good at stopping the running game (3.2) and pitch receiving (13.2 runs). Lucroy is next with 16.2 total runs saved thanks to his pitch blocking (4.0) and receiving (11.4).

Table 1: Catcher Runs Saved Leaders, 2014 (as of June 28, 2014)

Player	Team	Inn	Running Game	Pitch Blocking	Throwing Errors	Fielding Errors	Pitch Receiving	Total
Brian McCann	NYY	504	3.2	0.4	0.5	0.3	13.2	17.7
Jonathan Lucroy	MIL	623	0.2	4.0	0.7	-0.1	11.4	16.2
Buster Posey	SFG	473	0.8	2.8	-0.1	0.3	10.1	14.0
Miguel Montero	ARI	647	-0.5	1.5	-0.7	-0.6	14.0	13.8
Rene Rivera	SDP	311	2.4	-1.2	-0.3	-0.3	12.0	12.6
Russell Martin	PIT	375	1.7	-0.1	0.3	0.3	10.4	12.5
Hank Conger	LAA	330	0.9	-0.7	-0.0	0.2	11.5	11.9
Mike Zunino	SEA	572	1.5	-1.5	0.1	0.4	10.7	11.2
Ryan Hanigan	TBR	378	0.8	0.8	0.6	0.3	8.5	11.0
Jose Molina	TBR	341	-1.4	-0.6	-0.0	0.2	12.7	10.9
Jason Castro	HOU	513	-0.2	2.2	0.5	-0.6	8.9	10.8
Yadier Molina	STL	623	4.5	2.8	0.4	0.4	2.5	10.7
Alex Avila	DET	503	4.5	3.2	0.2	-0.2	-3.7	4.1
Travis d'Arnaud	NYM	358	-0.8	-1.7	-0.3	0.2	6.6	4.1
Robinson Chirinos	TEX	401	5.5	1.6	0.1	0.3	-4.3	3.1
Tyler Flowers	CHW	566	1.4	-3.5	0.1	-0.1	5.1	3.0
Yan Gomes	CLE	584	2.3	-2.1	-1.6	-0.1	4.2	2.7
Jose Lobaton	WSN	351	1.0	2.1	0.3	0.2	-0.9	2.7
Yasmani Grandal	SDP	323	-3.2	-3.1	-0.0	-0.3	8.1	1.5
Carlos Ruiz	PHI	580	-0.7	2.8	0.1	0.4	-1.4	1.2
Wilin Rosario	COL	434	0.3	-2.2	-0.1	-0.2	2.8	0.6
Devin Mesoraco	CIN	398	0.5	1.9	0.1	-0.2	-2.2	0.0
Salvador Perez	KCR	614	1.4	-0.6	0.1	0.4	-2.8	-1.4
Evan Gattis	ATL	483	-1.1	-3.8	-0.4	-0.2	2.9	-2.5
A.J. Pierzynski	BOS	495	-1.6	-0.2	0.2	0.3	-4.1	-5.4
Dioner Navarro	TOR	404	-1.3	0.8	0.1	0.3	-6.9	-7.0
Derek Norris	OAK	414	-3.0	0.7	0.1	-0.2	-5.2	-7.7
Welington Castillo	CHC	406	-2.8	0.3	0.4	-0.2	-6.2	-8.6
Chris Iannetta	LAA	388	0.5	-3.3	0.6	0.3	-7.5	-9.4
Kurt Suzuki	MIN	511	-1.1	2.8	-0.0	0.4	-11.6	-9.6
Jarrod Saltalamacchia	MIA	461	-2.8	-0.7	-1.5	-0.2	-6.2	-11.4

 Data sources: Baseball-Reference.com and BaseballProspectus.com