What's Cool on Campus - Data Analytics and Moneyball MLB and NCAA research

Part One: Open Letter to UNF Athletics: What's Cool on Campus - Data Analytics (UPDATE)

JACKSONVILLE, FL - After my initial research on the topic in September 2019, I continued researching data analytics adoption at the NCAA team level, adding six teams to the initial survey presented to the administration. 

The approximate 4 - 6 increase in wins over the trailing eight-year average of wins -- or wins expected -- remained in place. 

Growing number of programs finding or renewing success: (2019 record versus 2011-18 record)

Next, I looked at the final 2019 College Baseball poll from USA Today. I added a Y/N to sort for teams that had publicly announced their use of data analytics. An earlier USA Today article had mentioned Louisville and Texas Tech as College World Series participants that did not have an existing analytics program in place.  

from USA Today:
https://www.usatoday.com/sports/ncaa-baseball/polls/coaches-poll/2019/

2019 Final USA Today College baseball Poll - by adoption of data analytics

• Seventeen of the Top 25 teams had adopted data analytics (68% rate).
• Adoptees median number of Wins were 4 greater than non-adoptees, W% was 0.031 greater, which implies approximately +2 Wins. 
• Adoptees tended to play later into the post-season than non-adoptees.
• In other words, adding 2 - 4 wins while playing amongst the most competitive sub-set of teams you could select is meaningful. 

Next, I looked at adoption rate by conference. Here is where I feel the story began to expand and crystallize. 

I examined the Power-5 conferences (P-5) and selected Non P-5 conferences like the ASUN. This revealed the following:

• The Power Five conferences (P-5) adopted at a 55% rate versus 14% for Non P-5 conferences. 
• The SEC had adopted at 86% rate and the ACC at 64% rate, the highest rates observed. 
• The SEC is on track to hit 100% by 2021. 
• The Power 5 Conferences are on track to hit 100% by 2022.

     

Non P-5 schools, by contrast, are on an estimated adoption rate that mimics MLB's rate of increase (as shown below). This puts them on path to hit 100% by approximately 2032.  

That 10-year gap, if the Non P-5 status didn't do it, will relegate these schools to long-term, if not permanent, 'have-not' status in collegiate baseball. 

Year 1 = 1997 for MLB and 2017 for college baseball. 

Elitzur's MLB Timeline of Adoption (SABR teams over time)

These conclusions, if they hold true, are somewhat ironic in that one pillar of the Moneyball Theory and Dr. Elitzur's study, is that in MLB, poorer teams used data analytics to gain advantage against richer teams, in what was inherently an unfair contest between unequals. 

The analogy to college baseball flips that framework on its head in that richer teams are adopting it to further cement their advantage over less well-endowed competitors. 

 

Cum Wins v. Cum Payroll (1998-2016) - Avg. Payroll v. Diff. Wins vs. Expected Wins

Here is where things get interesting. I examined cumulative wins (from baseball-reference.com) versus cumulative payroll (from Lahman database) and applied conditional formatting to identify the top ten teams (green) and the bottom ten teams (red) by each category. The (white) cells are the middle teams per category. 

In the first category, cumulative wins and cumulative payroll, the Red Sox and Yankees (coded green-green) bludgeoned the field as top ten in payroll and top ten in wins. Not a huge surprise. 

Pittsburgh and the Rays (coded red-red) were bottom ten in both categories and that is not a huge surprise either. A 0.77 correlation between wins and payroll should come as no surprise either, it's the basis for the so-called "competitive balance tax" or de-facto salary cap. 

Cum. Wins v. Cum. Payroll (1998-2016) grouped by 10's

If you expand the conditional formatting here to show top ten-bottom ten-middle ten by category, you get some unusual pairings. 

Green-Green = BOS,LAA,LAD,NYY,SFG,STL

Green-White = ATL,CLE,TEX

Green-Red = OAK

White-Green = CHC,NYM,PHI

White-White = CHW,CIN,HOU,SEA,TOR

White-Red = ARI,MIN

Red-Green = DET

Red-White = BAL, COL, 

Red-Red = KCR,MIA,MIL,PIT,SDP,TBR 

This analysis looks at the absolute level of spending and the absolute level of wins. The top and the bottom level teams are segregated on that basis with only Oakland delivering on the upside as far as wins relative to payroll in aggregate which makes sense. They are the crown princes of Moneyball. 

Detroit finishing lower third delivering wins while spending in the upper third on the payroll side makes them the anti-Moneyball franchise so far.  

But is that the fairest way to grade franchise when the premise is to spend money efficiently rather than wastefully or recklessly?

Sum of Wins Over or Under Expected Wins and Avg Payroll Level

From the baseball-reference.com historical wins by team/year data, an "expected wins" field was created by weighting the prior three years win total, as is commonly done with projections. 

2019EW for example would be ((2018 W * 3) + (2017 W * 2) + (2016 W * 1)) / 6. 

OUW would then be the Over / Under of Actual Wins versus Expected Wins. 

I applied the same conditional formatting rules to sort in top-middle-low ten team buckets and calculated the following matrix of Wins Expected versus Average Payroll. 

Elitzur Wins -  Payroll Matrix

In my opinion, this gives a reasonable snapshot of which teams are more successfully employing the Moneyball concept of "doing more with less." 

High Wins over Expected with Low Payroll:

Oakland, Tampa Bay and Minnesota have had good success doing more with less. Arizona and Washington are also consistently on top pf their divisions. 

Low Wins over Expected with High Payroll:

Detroit and the Los Angeles Angels have had a bad run while spending at some of the highest levels. A bad combination recently.  

High Wins and High Payroll:

Boston, Chicago Cubs and Philadelphia fall into this category. High spending but high wins over expectations to match. 

High Wins with Middle range Payroll:  

Cleveland Indians with some consistently good years and the Houston Astros with some feast or famine years and a relatively new adoptee of data analytics (2012) scored well on Over/Under Wins, especially recently. 

This introduces another interesting observation which may appear to run counter to one of the earlier observations, that "if you were not in early, you were left behind."

MLB Wins over Expected Wins pre and post Adoption of Moneyball

               

click link above to see in Tableau:

Some teams who were late adopters of an analytics driven approach have had recent success in terms of wins above expectations (OUW):

Houston and Chicago Cubs (2012) and +8.63 / +3.06 OUW

Minnesota (2015) and +8.06 OUW

Philadelphia (2016) and Arizona (2017) +5.08 / +7.44 OUW

Houston and the Cubs have had good success but will regress somewhat as they add more years under adoption. Minnesota, Philadelphia and Arizona are more recent small sample size successes with only three to four years under adoption. 

Those team's recent successes, added to the previous success of the A's and the Rays, lend themselves to a belief that the teams that "need" to succeed with a Moneyball approach the task with a "have to have this work" mentality rather than a "nice to have" it work, if not throw some money at the problem. Failure is an ever-looming, existential threat to their success. This leads to a deeper commitment to the task and a greater buy in from everyone in those organizations. 

For the high payroll, high to middle success teams, data analytics is "nice to have" but not really "have to have". There is a safety net of the owners checkbook, an "in case of fire, break glass" option lesser well-endowed teams do not have, that blurs the amount of credit that should be given to data analytics for success in the W column. The low payroll teams are overcoming the 0.76 correlation between payroll and wins, the high payroll teams are surfing it to success. 

Each successive CBA defines the rules of engagement teams operate under and they naturally lend themselves to this type of stratification and perhaps always will. Organizations and staff at all levels are constrained by these rules and work with them and in some cases around them to the best of their ability in order to succeed.

On the collegiate side of the ball, college teams that lag in adoption could see some glimmer of hope from the late-adopters in MLB, however with the greater disparity and distribution of talent and the differences in the rules of engagement the NCAA and their member conferences set up between the teams, that glimmer of hope quickly morphs into a chasm of despair.  

Going forward, I would like to take more of a flow versus stock comparison, ie: change in Win Expectation versus Y-O-Y changes in payroll historically and see what that reveals.

References:

Elitzur, Ramy. “Data analytics effects in major league baseball.” (2020).

Open Letter to UNF Athletics: What's Cool on Campus - Data Analytics (UPDATE)

JACKSONVILLE, FL - Update: This was a presentation made to relevant stakeholders in the success of UNF Baseball in September 2019 regarding the need to utilize data analytics to improve the team's chance of success in the current environment. 

Of the first seven teams on the UNF 2020 baseball schedule, five of them (Rutgers, South Carolina, Central Michigan, Ohio State and Illinois State) use data analytics to improve their team performance. We posted a 1-9 record against the schools listed. 

We didn't make much headway convincing the current administration that this was necessary back then. However, as we see from our 2020 schedule, the trend of adoption by other schools has accelerated since we presented. 

"You can ignore reality, but you can't ignore the consequences of ignoring reality." - Ayn Rand

This is a look at UNF Baseball Historical W-L record. 

Here again, the trend is clearly not our friend. I don't even want to do a "What if " analysis or forecast sheet on when the program wakes up and realizes that this once proud program is playing to a sub- .500 Division I record. 

It's time to decide what type of program this is and what it wants to be going forward. Thankfully, there is not the pressure of a relegation system in place. Perhaps that would force action, but that shouldn't be the catalyst. Leadership and vision should have been. 

It's time to act and give these kids the tools they need to compete at the level the university chose to compete at. To do less is unfair to the kids. You cannot run a DI program on a DII budget. Finances should not be an issue as this isn't a particularly expensive proposition. 

What's Cool on Campus? Data Analytics!!

NEED:

What's Cool on Campus? Charlie Young and Illinois baseball analytics

http://www.news-gazette.com/video/2018-04-24/whats-cool-campus-charlie-young-and-illinois-baseball-analytics.html

Illinois and Elon, among others, have improved their programs as a result of the technologies. There are benefits to future sport management and statistics majors to help implement and maintain the systems and produce reports. The cost of tools, like Flightscope, Rapsodo Yakkertech and Hawkeye, are reasonable considering the potential benefits. I believe that within 5-10 years, virtually all baseball programs around the country will be using these tools to improve their teams economically. 

‘If You Don’t Have It, You’re Behind’: College Baseball’s Tech Arms Race

https://www.nytimes.com/2019/06/25/sports/baseball/college-world-series.html

“The technological wave that swept M.L.B. has reached college baseball, but the price of high-tech devices has created a bigger gulf between the haves and have-nots.” - www.nytimes.com

“Tech is the newest recruiting tool in Division I, the latest separator between haves and have-nots.”

https://www.forbes.com/sites/jamesayles/2019/06/24/how-baseball-teams-are-using-technology-to-change-the-game/#7e859e713b2f

Forbes Magazine seems to agree with this revolution. In an article about new technology in baseball, it says “Tech is the newest recruiting tool in Division I, the latest separator between haves and have-nots. 

Six of the eight schools that reached the College World Series — all but Louisville and Texas Tech — said they had purchased high-end analytic devices in the last two years

“Why Technology Defines the Future of Baseball”

http://www.yakkertech.com/2019/07/03/the-arms-race-has-begun-future-of-baseball-technology/

It doesn’t matter who you ask these days. Twins reliever Taylor Rogers was quoted on MLB.com saying that due to new technology he’s learned more in the past month than in the past four or five years.” Blue Jays righty Ryan Tepera says, “That’s the new phase of baseball we are in.”

“Technology Pioneers See First-Mover Advantage”

https://hbr.org/sponsored/2014/07/the-digital-dividend-first-mover-advantage

By far the most intriguing finding in the research is the correlation between the early adoption of new technology and company performance. Pioneers are growing faster than other companies and beating their competition. Twenty percent have experienced more than 30% growth—twice that of Followers and more than three times that of the Cautious. Firms that identified themselves as Cautious were the most likely to report no growth.

IMPACT / EFFICACY / CALL TO ACTION / URGENCY:

Growing number of programs finding or renewing success: (2019 record versus 2011-18 record)

Teams that are known adopters of a data analytics driven approach have averaged between 3.98 – 5.61 additional wins over their prior eight years average wins, a proxy for their “true talent level” or expected number of wins per season. 

Nine of Eleven (82%) teams experienced success and two (18%) had down years. Of those that experience gains in wins, those gains ranged between (1.3 – 19.0) additional wins and averaged (5.61) additional wins.

From UNF baseball’s perspective, their eight-year prior record averaged 33W - 23.5L, a plus or minus swing of 5 games results in either an expected record of between 38-19 or 28-29. A record of 33-24 would be the mid-point, approximately where the team finished in 2019.  

The future options are somewhere between a team with legitimate post season aspirations and potential Top 25 ranking and one that has little or no post season expectations, with maintaining current position in the conference, a possible, but not attractive third option based on the fast changing environment.   

Two of nine conference foes have technology (22%) which mirrors estimate of penetration in Division I schools overall. Twenty-two percent equals 66 of 300 DI schools, basically equal to number of teams in the Power Conferences.

This is when the train is moving slow and you can still jump on board. The next stage, when early majority and late majority schools get on board, competitive gains will slowly erode to zero and then negative.

In terms of the 300 DI baseball schools, UNF ranks right somewhere between the 40-50^th percentile based on recent performance. As shown by the technology adoption curve below, UNF ranks somewhere between early and late majority adopters (50^th percentile). The pace of adoption from 22% to 50% penetration will accelerate and likely not take another 2-3 years.

As both the NYT and Forbes articles above suggest, within another two to three years, the opportunity to be a leader or an early adopter will have been lost.  The train will have left the station; laggards will be punished by their faster acting competition (see Technology Adoption Curve illustration below).

Accelerated by digital: A timeline of technology adoption curves, shifts in industry are exponential not incremental. Stand out or step back is the by-word.

https://www.accenture.com/us-en/insights/strategy/platforms-driving-industry-x0

Rogers Technology Adoption Curve meets Elitzur’s decreased comparative advantage

URGENCY:

“Moneyball advantage peters out once everyone's doing it” - author Ramy Elitzur, the Edward J. Kernaghan Professor of Financial Analysis and associate professor of accounting at the University of Toronto's Rotman School of Management.

Paper shows baseball data analytics only an advantage when few used it

https://www.sciencedaily.com/releases/2019/04/190408161654.htm

When you have a secret sauce and nobody else knows about it, you have a competitive advantage. Once the secret sauce was outed, which was what happened with the book, everybody could imitate the Oakland A's."

Dr. Elitzur created a database for the study, inputting information from 1985 to 2013 about team payrolls, playoff success, the spread of data analytics use, and players' overall contributions to their team, represented by a key statistic from Moneyball's "sabermetrics," -- the type of data the Oakland A's used to identify lower-priced, undervalued players through statistics such as how much time spent on base.

He found that between 1997 and 2001, there were only two "Moneyball" teams in the MLB. Another three teams had taken up the practice by 2002. 

By 2013, more than 75 percent of MLB teams were using it. Sabermetrics gave teams the strongest advantage up until 2003, the year Moneyball was published. 

By 2008, the comparative advantage was lost as more and more teams adopted sabermetrics. The practice of data analytics also spread beyond sports, to business and government.

Other /ancillary benefits created between teams and schools within the university:

Baseball…………………..……....………………..Softball

SPM (COEHS)..………................. MATH/STATS (Coggin)

UNEXPLORED OPPORTUNITIES – CLUBS ON CAMPUS

Tracking data takes time and time is limited, explicitly by the NCAA and by the volume of tasks coaches must take on just to keep programs afloat and on-budget.

However, there are students right now who are:

1.            Analytically-minded

2.            Willing to work for free (class projects, practicums and internships)

3.            Love baseball

Students can be found in most school’s computer science/math/economics clubs. This is an area of opportunity that small schools have that is underutilized. Aid and assistance from students / clubs, in addition to generating buzz for the teams and the university can help baseball the most, given the recent rejection of a third paid assistant coach.

Player development analytics is currently a wide-open field. No MLB team is going to make their sensitive, proprietary player data available to the public. Talented analytics people, those who aspire to the MLB analytics jobs of the future, don’t have a lot with which to work.

Schools can develop this as an opportunity zone for students. A couple of seasons of analyzing player data adds real-world experience to enhance their resume for MLB internships / jobs. The insights they can uncover helps the team win games. It’s a win-win situation.

THE REVOLUTION REACHES COLLEGES….and SOFTBALL!!

http://www.yakkertech.com/2019/07/23/player-data-revolution-is-coming/

This revolution has continued to spread, most recently reaching college baseball. Upwards of 50 colleges are collecting in-game data. Another 40 to 50 have bullpen units to assist with improving pitching. BaseballCloud has emerged as a great database company with analytics to assist the coaches. The momentum is clearly there now, and teams are all looking for technology they can implement to improve their team.

The data revolution is now starting to be recognized in softball. At least one major program is installing an in-game system. 

Softball academies are popping up, as are large facilities for holding very large tournaments – and these facilities are interested in in-game systems as well. The price point is reasonable, and coaches are understanding the value for player development.

“When performance is measured, performance improves. And when it is then reported, improvement occurs again.”

These coaches leading the data revolution in softball also see the value in being able to better recognize high school players for recruiting.

At Yakkertech, we are so excited to be a participant in supporting softball programs. Our in-game and bullpen system are ideal for enhancing player development. So to all the folks in the softball world – enjoy the revolution! It’s here!

http://www.yakkertech.com/2019/07/30/player-development-where-and-how/

Player development is a key element in baseball. There is one somewhat simple metric that shows the changes now in the “how and where” of the development of young, promising players. In the past 10 years, the percentage of players chosen in the first ten rounds of the draft from colleges has gone from 52% to over 75%.

While some colleges are also using technology and more scientific methods to enhance player development, they are still hounded by W’s and L’s to validate their existence as a coach. But that being said, many colleges now see that they can enhance performance of their players by having the technology and priority to use to make their players better……..Academies and colleges – that’s where players are obviously getting better……Makes things easier for the MLB, and they obviously see it!

BUDGET – FEASIBILITY & CONSTRAINTS:

https://www.drivelinebaseball.com/2018/08/data-driven-budget-baseball-analytics-high-schools-colleges/ 

TIER 2 – SMALL TO MEDIUM INVESTMENT

These can be applied in stages, one per year. Or, with a dialed in process for collecting data, you can do a big fundraiser to scrape together the $10,000-$15,000 it will take to purchase this all at once.

Note: @ $9K - $27K expenditure per sport (for Rapsodo H&P + Flightscope) implies approx. $1.8K-5.4K depreciation/replacement expense per year

References:

Elitzur, Ramy. “Data analytics effects in major league baseball.” (2020).

The List: Eight Prospects With Strike Zone Red Flags - BaseballAmerica.com

As the article mentions, plate discpline stats are not the be-all or end-all when evaluating prospects development. But this article is from four years ago and only the top three of eight has made even a minimal impact at the major league level, and arguable only one has made a substantial impact. Why is this? It's likely because, as in the accuracy argument for QB's making the transition from NCAA to the NFL, if you can't do it at the lower level -- where pitchers are also struggling with command and control issues -- you won't be able to succeed giving away pitches and AB's at the major league level. They will exploit your weaknesses better than A level or even AA level ball. 

I agree with the title of this article, like with QB's it is a red-flag. 

from BaseballAmerica.com
http://www.baseballamerica.com/minors/8-hitting-prospects-with-strike-zone-red-flags/?_wcsid=8EB162800D50BAD2AB74003610640D767EBA8CD553241916#HbgdBAvqOIhUkXqe.97

THE LIST: EIGHT PROSPECTS WITH STRIKE ZONE RED FLAGS

It's still early in the minor league season, but we're at the point where we have meaningful data on players, both in terms of their performance and traditional scouting methods.

For hitters, the ability to control the strike zone is crucial. Whether it's plate discipline, pitch recognition or just swinging and missing through too many pitches in the strike zone, many promising prospects with tremendous raw tools have never been able to make the next step because of their inability to hone the strike zone.

That doesn't mean every minor league hitter with a low walk rate or a high strikeout rate is a bust waiting to happen. Pirates outfielder Starling Marte is an example of a player who never walked much in the minors but has become one of the best players in the game. And we have seen plenty of minor league hitters whose best skill was their ability to draw a walk never have success beyond the minors.

So while plate discipline is not necessarily a make-or-break factor in predicting a prospect's future, it's a red flag when hitters are having trouble recognizing spin, fishing for too many pitches outside the strike zone or are getting beat in the zone with stuff exposing holes in their swing. Hitters can certainly develop and cut down on those holes, but the pitching they're going to face only gets better as they move through the minors.

The hitters listed below are all examples of talented prospects who have red flags in their game related to their ability to control the strike zone. Some of these players are struggling, while others are off to seemingly great starts but have underlying issues that are cause for concern going forward.

1. Tim Anderson, ss, White Sox

Plate discipline has long been a concern with Anderson, but even with his aggressive approach he shined last year upon his jump to Double-A Birmingham. This year in Triple-A Charlotte, one of the most favorable parks in the minors for hitters, Anderson is hitting .287/.310/.380. Anderson is a premium athlete with plus-plus wheels and quick bat speed, but with five walks and 38 strikeouts in 155 plate appearances, his free-swinging tendencies hamper his ability to get on base.

2. Daz Cameron, of, Astros

Cameron signed for $4 million as the No. 37 overall pick in last year's draft, but the early returns have been ugly, to the point where the Astros sent him back to extended spring training after May 1. Cameron has swung and missed liberally, with 33 strikeouts in 87 plate appearances (38 percent), eight walks and a .143/.221/.221 overall line for low Class A Quad Cities. The good news is Cameron has still looked excellent defensively, with multiple diving catches. And if Cameron needs inspiration for how to turn things around after struggling in the Midwest League, he can just ask his father, Mike, who batted .238/.292/.297 in 122 games for South Bend as a 20-year-old, then eventually became one of the game's premier center fielders.

3. Austin Riley, 3b, Braves

Riley got off to a torrid start in his pro debut last season, batting .304/.389/.544 with 12 home runs in 60 games between two levels of Rookie ball. Riley has big-time raw power, but low Class A pitching has exposed more holes in Riley's swing, with nine walks and 44 strikeouts in 137 plate appearances for a .248/.299/.400 line overall.

4. Jake Gatewood, 3b, Brewers

Size can be a double-edged sword for hitters. In general, the bigger hitter will usually have more power potential than the shrimp, but at a certain point, being too tall works against a hitter. Being taller means the hitter has a larger strike zone he has to cover, and with longer arms often comes a longer swing with more holes. Gatewood has serious raw power, but he's hitting just .267/.277/.444 in 137 plate appearances. His free-swinging, all-or-nothing approach holds him back, with only one walk and 41 strikeouts for low Class A Wisconsin.

5. Monte Harrison, of, Brewers

When the Brewers drafted Harrison out of high school in the second round of the 2014 draft, he looked like a player who could combine top-shelf athleticism with a patient hitting approach to develop into a dynamic prospect. Instead, Harrison has been held bag by injuries and excessive strikeouts. Harrison's bat speed, foot speed and arm strength are still impressive raw tools, but he has hit just .160/.243/.210 with nine walks and 39 strikeouts in 112 plate appearances in the low Class A Midwest League.

6. Eric Jenkins, of, Rangers

The Rangers rolling the dice on a raw, toolsy, high-upside high school player with one of their top picks? That sounds familiar. Nick Williams, Joey Gallo and Lewis Brinson have all broken through as premium prospects, and while Jenkins doesn't have the raw power to match any of those three, he's a dynamic athlete with plus-plus speed. Rangers minor league hitting coaches have done a stellar job getting prospects to cut down on strikeouts, something Jenkins will have to do as he's hitting .206/.263/.298 in 158 plate appearances with 11 walks and 48 strikeouts for low Class A Hickory.

7. Javier Guerra, ss, Padres

Guerra doesn't have to be a prolific hitter to be a valuable player. He's a plus defender at shortstop, with smooth actions, a quick first step and a nose for the ball to go with a plus arm. As long as he can be serviceable at the plate, Guerra can be an everyday shortstop. To do that, Guerra will have to stop chasing so many pitches outside the strike zone. He's hitting .221/.280/.359 for high Class A Lake Elsinore, with 11 walks and 46 strikeouts in 144 plate appearances.

8. Travis Demeritte, 2b, Rangers

On the surface, Demeritte looks like he's in the midst of a breakout season, batting .278/.366/.677 with 12 home runs in 35 games for high Class A High Desert. Demeritte's quick hands and plus raw power are legitimate, and he has taken a step forward from where he was a year ago. Yet High Desert is still a launching pad, and once he leaves there, the underlying swing-and-miss issues and chase tendencies will get magnified. His 18 walks in 153 plate appearances aren't a problem, but the 53 strikeouts (a 35 percent K-rate) are a concern.

Sent from my iPhone

Listen to: Training the motor- a template for bat speed development and proprioception | Ahead of the Curve Jonathan Gelnar

On Wed, Dec 4, 2019 at 3:30 PM Charles Slavik <theslav1959@icloud.com> wrote:

Listen to Training the motor- a template for bat speed development and proprioception. from Ahead Of The Curve with Jonathan Gelnar on Apple Podcasts.

https://podcasts.apple.com/us/podcast/ahead-of-the-curve-with-jonathan-gelnar/id1256849644?i=1000449051840

Sent from my iPhone

Transcript:
https://medium.com/@jgelnar7/training-the-motor-a-template-for-bat-speed-development-and-proprioception-a64de513ca53

Related Article:
Reverse Engineering Swing Mechanics from Statcast Data by David Marshall

September 14, 2017
https://community.fangraphs.com/reverse-engineering-swing-mechanics-from-statcast-data/

The Anatomy of GREAT AB aka: Buster Posey Fights for His Pitch | FanGraphs Baseball

Lost in the minutiae, just like the Giants greatness at times. The casual fan turns off or clicks to another channel after about 2 or 3 foul balls and misses the greatness that was embedded, but well hidden, in this AB. Like a pearl in a shell.

This AB is a mini-clinic on having a good hitting approach and strategy, plate discipline and batting eye as well as tough as nails two-strike hitting

from FanGraphs Baseball:
Buster Posey Fights for His Pitch | FanGraphs Baseball:

When a hitter gets into a two-strike count, his mission is to protect the plate. The expectation is that he’ll swing at anything close, so that he doesn’t strike out looking. You look stupid when you strike out looking and nobody likes it. This outside fastball was very close and Posey didn’t swing at it. It was a ball, it was definitely a ball, but it was almost a borderline strike, and there have been worse strikes before, probably even called by this very umpire. Posey didn’t swing at it. Had this pitch been called a strike, some fans might’ve been upset at Posey for not protecting. This pitch was called a ball and we wonder instead if Posey has just the most amazing eye in the universe. Results-based analysis allows us to label this a spectacular take. Blanton executed perfectly. Posey did the right thing, probably. Posey definitely did the right thing in hindsight.

'via Blog this'

The author ends his piece with "Great at bat by Posey. He seems to have a lot of those." Now you know why. Sometimes greatness in baseball is disguised or hidden beneath the surface and what seems to be inaction to the casual observer is really greatness in disguise. I think Paink and Belt show similar approaches and discipline and consequently they throw out great AB's on many occasions as well as Posey. Matt Carpenter of the Cardinals is another one from the opposite side of the field. They don't back down or give in with two strikes and they certainly don't just swing from the heels. They hunker down and battle.

If you have a lineup full of these type of guys you can grind down and wear out even the best pitchers. It seems like the Yankees and Mariners in the early 2000's were loaded with guys like this and go figure, both teams won a lot of games.


More from the article:

Okay, it’s 1-and-2. Blanton has thrown a first-pitch curve for a strike, a low changeup for a ball, and a slider for a strike. Already he’s given Posey a different look.

It’s hard to tell from the camera angle, but this is a fastball down and in, tucked just inside the corner of the zone. It looks like the pitch was supposed to be just a little more inside, to tie Posey up, but it wasn’t in a bad spot, and a foul was about the best Posey could’ve hoped for in a defensive situation. Posey couldn’t cheat by sitting on a fastball while behind in the count.

The thing about most curveballs is that they aren’t really swing-and-miss pitches, like you’d think they might be. They disrupt timing and frequently catch hitters looking. From his body language, Blanton probably hoped this was a swing-and-miss curveball. It was perfectly located, low, and just off the plate. Posey wound up ahead of it and barely got a piece. A piece was all that he needed to get to keep himself alive.

This is a pitch that was quickly forgotten, given the way the at-bat wound up. Ahead 1-and-2, Blanton missed with a fastball and gave Posey a heater right down the middle of the zone. This was presumably not one of the put-away pitches to which Blanton was referring. This was a mistake, but because Posey probably still had offspeed pitches in his mind, he couldn’t get the swing he’d like to get on this pitch. He stayed alive, though.

When a hitter gets into a two-strike count, his mission is to protect the plate. The expectation is that he’ll swing at anything close, so that he doesn’t strike out looking. You look stupid when you strike out looking and nobody likes it. This outside fastball was very close and Posey didn’t swing at it. It was a ball, it was definitely a ball, but it was almost a borderline strike, and there have been worse strikes before, probably even called by this very umpire. Posey didn’t swing at it. Had this pitch been called a strike, some fans might’ve been upset at Posey for not protecting. This pitch was called a ball and we wonder instead if Posey has just the most amazing eye in the universe. Results-based analysis allows us to label this a spectacular take. Blanton executed perfectly. Posey did the right thing, probably. Posey definitely did the right thing in hindsight.

Back to work. Blanton throws Posey a fastball tucked into the low-away corner. Maybe a little too over the plate, but not that badly over the plate. Posey knows to protect this time, because the pitch looks like a strike, or it looks like it could be called a strike. Foul ball. Tough pitch to hit; maybe the next one will be better. That’s the idea of the whole at-bat, basically. Tough pitch to hit; maybe the next one will be better.

I still can’t quite figure out how the at-bat didn’t end with a strikeout right here. This is a changeup, low, out of the zone, just over the outer half. It begins away and tails back over the plate, like a backdoor changeup, and also there’s the part where it was low and out of the zone. This is a strikeout pitch. I suppose it could’ve been more low, but it was sufficiently low to generate a swing and miss. Posey gets out in front and gets a piece. Tough pitch to hit; maybe the next one will be better.

Kablammo! “M-V-P” chants. “Beat L-A” chants. Starting to think that Blanton doesn’t only do the little hop when he thinks he’s getting a swing and miss. This is the very definition of a hanging slider. Instead of being thrown to a good spot, this slider is thrown to pretty much the worst possible spot, up and over the middle of the plate. I wouldn’t say it looks like a homer off the bat, but it looks like it might be a homer, and indeed it was a homer. Posey was working toward this, and after fighting off a bunch of pitcher’s pitches, he took advantage of a hitter’s pitch.

To review:

Old-timey baseball wisdom asserts that a hitter gets one pitch to hit in any given at-bat. Of course that isn’t always true, and it would be outrageously bizarre if that were always true, and here you could say that Posey got two good pitches to hit, even after falling into a two-strike count. The second pitch to hit was much much more hittable than the first one and Posey made no mistake. It was Blanton who made the mistake, after having executed so effectively before.

The temptation is to believe that Posey did this on purpose. That he kept fighting pitches off so he could live to see another. I’m guessing Posey wasn’t trying to just foul off all those pitches, but it’s to his credit that he could anyway. For the most part Blanton did what he wanted and he couldn’t make Posey go away until Posey made himself go away after jogging in a circle. Buster Posey kept himself from striking out when he easily could’ve struck out, and eventually, a pitcher will make a bad mistake. No pitcher can hit his spot every single time. Sometimes even the best command pitchers will miss by a foot, or more.

And that’s the story of how Buster Posey hit his 20th home run of the season. Draw all the parallels to the NL West race that you like. The Dodgers got off to a quick start, but they couldn’t put the Giants away, and the Giants ultimately vaulted ahead. I’ve said before that everything is something else in a nutshell, and this Blanton vs. Posey at-bat is most certainly included in everything.

Great at-bat by Posey. He seems to have a lot of those.

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