Matt Garza vs. Ricky Nolasco: The tail of the tape from their July 3rd games

Tonight was likely a showcase of the two most sought after pitchers of the 2013 Trade deadline. Both pitched against top of their division teams.

• Garza faced the leagues' most patient lineup (Oakland tied with Boston for walks at 319) 
• Nolasco faced the leagues most swing-and-miss happy bunch (Atlanta is 2nd to last at 731)

The best way to determine the quality of pitching or stuff is by using Brooks Baseball and their Pitch F/X graphs.

Ricky Nolasco performance by pitch velocity against Atlanta:

Fastball between 90-92 starting out, throwing a mix of 2-seam and 4-seam, 75% (40 total) 4 seamers.
But the velocity after pitch 43 never returns. From then on, he goes to the slider for his swing and misses, throwing 29 sliders, getting 12 swings and misses. This is smart of Nolasco - the Braves love his fastball because it is BP speed (they missed only 4 of those), and Nolasco, can't nibble with it tonight. He mixed in enough Curves and Splitters to give the Braves fits as the game progresses.

Nolasco used a slide step to get 1.2-1.25 to home, but he ditched that around the 4th, the same time his velocity dropped. Get on base, he will have to concede the stolen base while using breaking stuff. I just don't think he can hump it up there. (NOTE: it was raining pretty consistently. However, pitchers deal with stuff like that always, adversity.)

By comparison, Atlanta's Mike Minor velocity was fairly consistent until his blowup in the 5th, where he threw 32 pitches, and obviously tired. Which is the GREATER point: Nolasco was not able to sustain his fast ball velocity past 50-60 pitches, making him vulnerable, tonight.

In his prior start, June 28th, Nolasco was better at sustaining his velocity because he was on 6 days rest. The start before that, June 21st, the real Ricky Nolasco tired at pitch 76, never returning upwards to his beginning velocity.

In short, Nolasco has a 5-inning, 75-pitch window to get you out, using his slider as his best weapon, if he can always throw that well. He might as well figure out how to throw more sliders. No matter what reporters print (Miami Herald), Nolasco is not ready (yet) for a big time playoff battle.

Nolasco's Pitches against Atlanta, July 3, 2013

Pitch Type	Avg Speed	Max Speed	Avg H-Break	Avg V-Break	Count	Strikes / %	Whiffs / %	SNIPs / %	Linear Weights
FT (TwoSeam Fastball)	90.27	91.75	-8.09	4.80	11	6 / 54.55%	1 / 9.09%	3 / 37.50%	0.0342
FF (FourSeam Fastball)	90.22	92.1	-4.68	6.84	31	20 / 64.52%	3 / 9.68%	12 / 52.17%	-1.0115
CH (Changeup)	84.11	87.55	-7.48	2.44	4	2 / 50.00%	1 / 25.00%	1 / 33.33%	-0.2668
SL (Slider)	81.64	83.98	1.63	-0.42	29	22 / 75.86%	12 / 41.38%	17 / 70.83%	-1.9255
CU (Curveball)	73.96	76.5	9.18	-7.23	13	7 / 53.85%	3 / 23.08%	6 / 50.00%	-0.6200
FS (Splitter)	80.22	88.28	-4.60	2.61	8	5 / 62.50%	2 / 25.00%	3 / 50.00%	2.0335

Matt Garza Pitch Speed at Oakland, July 3rd

The conditions were better for pitching, no rain to fight.

Garza's graph shows the ability to reach back and get a bit more for a leverage situation. As late as pitch 101, Garza hits above 94 MPH. This was at the end of 7 innings against a patient, never unwilling-to-take-a -walk, Moneyball lineup.

Garza's only mistake: throwing back to back curves at pitch 54-55 to Moss, who hit a home run. Luckily, the Cubs scored off Bartolo Colon.

Garza throws usually nothing but Fastballs, he sinks one, or two, then a 4 seam, off the plate, up or strategically placed for pop-ups. Garza gets more vertical break (sink, but Nolasco gets more run, horizontally). The add/subtract works, and his range on both types of heaters, and top speed makes good hitters uncomfortable enough to be late, or just not able to get hard liners off him.

He doesn't miss that many bats. But neither does Nolasco. Garza's slider is above average when he surprises hitters, since they see about 85% fastballs. His curve can work well too, but he hangs 1 out of 4 of them. So, he doesn't usually throw many, and shouldn't to anyone with a lick of power.

Garza's pitch types at Oakland, July 3rd

Pitch Type	Avg Speed	Max Speed	Avg H-Break	Avg V-Break	Count	Strikes / %	Whiffs / %	SNIPs / %	Linear Weights
FT (TwoSeam Fastball)	93.08	95.7	-5.98	9.16	49	32 / 65.31%	6 / 12.24%	21 / 55.26%	-2.0286
FF (FourSeam Fastball)	93.59	95.68	-3.45	9.74	41	25 / 60.98%	2 / 4.88%	18 / 52.94%	-1.0846
SL (Slider)	85.71	87.39	1.23	2.07	18	11 / 61.11%	4 / 22.22%	8 / 53.33%	-0.9448
CU (Curveball)	74.85	76.59	4.60	-8.90	6	3 / 50.00%	0 / 0.00%	1 / 25.00%	1.2033

His prior start confirms too his velocity hovers right at 94 MPH. He loses only a fraction past 85 pitches, but puts 2 or 3 more fastballs above 94 at around 100 pitches, throwing a few more curve balls and sliders to a weaker Milwaukee team, getting by with it.

 Garza's velocity at Milwaukee June 27, 2013

The Final Analysis

Matt Garza is much, much better trade piece. He has been gradually stretched out his last 3 starts from 98 - 101 - 114 pitches, mixing some of his ancillary stuff, while showing consistent velocity. Nolasco, even on long rest, has suspect velocity once past 75 pitches. His fastball is very hittable for good FB hitting teams, like Boston, Oakland, Texas, or the Los Angeles Dodgers, playoff contenders all.

Garza should be the jewel piece of the trade season, if a team wants a better chance, than if they select Nolasco.

MLB Franchise Success: A breakdown of a few ideas

Before I go into my diatribe, two friendly bloggers to tag:

Another Maria

Dwacon - http://dwacon.blogspot.com (post kept screwing up)

MLB Baseball has various objectives:

1. Revenues/profits
2. Home Attendance
3. Championships
4. Winning Regular Season Games

One of the best tools to determining a correlation is coefficient of determination.or R-sq. This number is extremely useful in determining the close correlation between two variables. A number above .9 is excellent. .75 is very good. .55 and above is adequate to good. and anything above .4 can reflect a moderate correlation. Zero to .15 correlation shows little or no reflection of a match.

Revenues to Home Attendance (1998-2005): R-sq= .596. This is a adequate to good correlation between these factors. It is no surprise given more people equal more revenues. Factors such as Team marketing, Publicity and TV contracts would (or should) strengthen this, but data is unavailable for that.

Championships to Winning Regular Season Games: It takes regular season wins to go on to the playoffs. There, it is chance and chance alone (Billy Beane's comment: "My shit does not work in the playoffs,") reflects that. Yet, the Ability to win games is tied to Run Differential (R-sq: .97).

Runs Scored: Requires two factors: OBP% and SLG%. On Base % is nearly three times as important as Slugging Average. R-sq is greater than .9, reflects an importance in these factors.

Stolen Bases have nearly zero correlation. The best analysis of a excellent running team(s) - the 1980's St. Louis Cardinals - shows that they generated only 10-12% of total runs scored via the Stolen Base. Without a success rate of 75% or greater, the value is minimal or negative.

Runs Allowed:The biggest breakthrough came when Voros McCracken devised two new metrics:

BABIP and DIPS.

Batted Average on Balls in Play are tied mostly to the fielder's ability to catch the baseball in play. A pitcher, though partly responsible for these hits (line drives, grounders, flies), can not position or improve his fielder's natural ability to succeed. If scouting the other team's hitters and pitch-to-pitch placement succeed then this average will likely go down. If this value is low and DIPS is favorable, then a Pitcher's ERA will likely be lower.

It turns out 4 factors have a great deal to do with a pitcher's success (DIPS) independent what his fielders can do (BABIP.)

• High Strikeouts ( less balls to field, easier to play defense)
• Low Walks (less runners, less opportunities to score, less disruption of pitcher and fielders)
• Low Home Runs (Or solo shots, these are guaranteed runs)
• Less Hit Batters (tied to walks - but it could be used as a tool to deter)

With these two factors, R-sq can be as high as .77. JC Bradbury, The Baseball Economist, 2007, pg 171.

The biggest ways a team improves are through:

• Trades
• Drafting
• Free agency

It takes talented GMs, Managers, Scouting and Stat Gurus to accomplish the goal of assembling good to great talent.

Much of this concept can be explain in this excerpt from my work in progress project Bringin' Gas and Dialin' 9:

As a primer to this subject (since it included much of Bill James’ astute statistical analysis) is that much of the actual premise behind Michael Lewis’s research and writing in Moneyball was that the Oakland Athletics of the late 1990’s and early 21st century utilized this statistical knowledge to build their teams judiciously (and frugally) in the high stakes baseball scouting market that reflected a higher regard for speed for speed sake, the rare 5-tool players (Batting for Average, for Power, Speed, Arm and Fielding Excellence) and drafting younger players out of high school that were somewhat difficult to project 5-7 years later.

Whereas, Oakland, spent more time looking at key statistics to find unusual selections in their drafts, utilized low cost free agents that still retained either a power component or on-base component that could also fit piece meal into their lineups because they had also lost players due to high salary demands (such as Jason Giambi moving over to the Yankees after winning the AL MVP, Johnny Damon to the Red Sox, or All-Star shortstop Miguel Tejada to Baltimore) and also drafted college pitchers that could immediately be used at the major league level because they met defense independent pitching statistics (DIPS) criterion, such as Rookie of the Year closer Huston Street in 2005.

As Paul Caron and Rafael Gely reflect in a similar vein in a 2004 Texas Law Review article comparing the legal education ratings of law schools and Major League Baseball:
“Moneyball paints an intriguing portrait of how Billy Beane’s “superior management” allowed the Oakland A’s not only to compete with, but also to prevail over, teams with double or even triple the resources. Beane realized that Major League Baseball was rife with inefficiencies that he could exploit. These inefficiencies derived from baseball’s reliance on subjective evaluation of players by scouts, as well as objective evaluation using conventional Triple Crown statistics, to measure players’ contributions to a team’s success. Beane disdained the view that you could evaluate players by watching them play and instead tapped into an alternative body of statistical data to more accurately value players that other teams either under-or over-valued using the traditional measures. In the case of hitters, Beane displaced the traditional Triple Crown statistics (batting average, home runs, and RBIs) with “OPS,” which combines a player’s on-base percentage (“OBP”) and slugging percentage (“SLG”) in measuring his offensive value to a team. In the case of pitchers, Beane discarded two of the three Triple Crown statistics (wins and ERA) in favor of “DIPS,” defense independent pitching statistics, which attempt to strip away the effect of a team’s defense on a pitcher’s performance by focusing on those statistics exclusively within a pitcher’s control: walks, home runs, and strikeouts.

Interestingly, these alternative statistical methods did not arise from within Major League Baseball itself. Instead, Lewis traces the lineage of these new ways to evaluate players to Bill James, at the time a night watchman in a pork and beans factory. In 1977, James self-published a sixty-eight-page book that turned into an annual “abstract” that looked at player performance through new statistical lenses.”[1]

With Bill James’ innovative insight, two decades later, the Oakland A’s were beating the long odds that most teams faced having 1/3 to 2/5 of the financial wherewithal as the Yankees had, but winning nearly the same amount of games in the regular season. But this fact was only brought to a greater public light with the publishing of Moneyball.

Once again, in Moneyball, Michael Lewis reports that Bill James also stated, “college players are a better investment than high school players by a huge, huge, laughably huge margin.”[2] And the Oakland Athletics have pursue this particular motto due to ownership constraints on signing bonuses in the picking of their top players and their firm beliefs in their drafting practices. They have gotten immediate returns on college players, such as Joe Blanton, Nick Swisher and Huston Street; which is exactly the formula they look to compete with for years to come. Primarily, they [the Oakland A’s] have utilized hard and real statistical analysis and college performance versus gut feel or sensed potential.[3]

In quoting Michael Lewis, [Paul Caron and Rafael Gely] in a 2004 Texas Law Review address the Legal Profession’s similarities to this Moneyball analysis in referencing, “ ‘Everywhere one turned in competitive markets, technology was offering the people who understood it an edge. What was happening to capitalism should have happen to baseball: the technical man with his analytical magic should have risen to prominence in baseball management, just as he was rising to prominence on, say, Wall Street.’ But real general managers, as contrasted with their fantasy counterparts, obdurately refuse to embrace the statistical measures of players’ contributions to teams’ success and thus create enormous inefficiencies in the Major League Baseball market for players.”[4] This is very nearly the Core Mantra of the Beane Philosophy: Exploit inefficiencies in evaluation of players through statistical analysis, utilize certain predictive measures (such as OBP*SLG 90%+ correlation to Runs Scored) and price it according to (and favorably against) the existent market forces in MLB.
[1] Caron, PL, Gely R. What Law Schools Can Learn from Billy Beane and the Oakland Athletics. Texas Law Review Vol. 82 (1483). 2004. 1491.

[2] Lewis M. Moneyball: The Art of Winning an Unfair Game. New York: W.W. Norton & Company; 2004. 99.
[3] http://www.protrade.com/. 2002 'Moneyball' Draft Revisited.http://www.mlb.com/; 2006 June 6.
[4] Caron, PL, Gely R. What Law Schools Can Learn from Billy Beane and the Oakland Athletics. Texas Law Review Vol. 82 (1483); 2004.1493-1944.