WAIBI DISSECTION

WHY IF ASTROS HAD USED WAIBI SABERMETRIC SYSTEM, THEY WOULDN’T HAVE NEVER HIRED GURRIEL  FOR 47 MILLIONS?

EL SÍNDROME DEL OPORTUNISMO SEGÚN WAIBI - (Primera Parte)OPPORTUNISM SYNDROME BY WAIBI (Part One )

En esta primera parte hacemos referencias a:

En esta primera parte hacemos referencias a:In this first part we make references to:

• Performance analysis from WAIBI standpoint
• ormance de Yulieski en los eventos más difícilesYulieski’s performance in the toughest events
• El Síndrome de OportunismoThe Opportunism Syndrome
• Astro’s doubts, Waibi’s answers
• La Medicina y el método de análisis de Waibi.Medical Sciences and Waibi analysis method. El Diagnóstico del jugador y la Solución del problema.Player Diagnosis

Nuestro pronóstico se va cumpliendo con Gurriel, como sucedió con Abreu.Our forecast has being accomplished with Gurriel, as with Abreu. Cito: “Esperemos que podamos ver brillar finalmente a Yulieski en un torneo de máximo nivel, aunque nuestro pronóstico no es que suceda esto . I quote: "We hope to see Yulieski finally shining in a top-level tournament, even though our forecast is he will not. Solo PODEMOS ASEGURAR que, a pesar de que en Cuba Yulieski rompió varios records, y Abreu muy pocos, Yulieski no logrará emular las grandes actuaciones de su compatriota en la MLB. What we can say is that, although in Cuba Yulieski broked several records, and Abreu very few, Yulieski will not achieve to emulate the great performance of his compatriot in the MLB. Y es probable que tampoco la de otros como Puig, Ramírez o Céspedes . And it's probably he can´t emulate others like Puig, Ramirez or Céspedes.”(publicado el 5 de agosto de 2016) (published on August 5, 2016, after Gurriel’s first MINORS League game, clase A)

Seguimos insistiendo en nuestro concepto fundamental. Waibi Sabermetrics es un sistema de estadísticas de selección , y no de estadísticas de rendimiento (históricas).We continue to insist on our fundamental concept: Waibi Sabermetrics is a system of  selection stats, not a performance (historical) stats one. Las estadísticas hasta hoy, incluidas las sabermétricas (al menos las públicas) pertenecen casi en su totalidad al segundo grupo, pero son utilizadas como si pertenecieran al primero. The statistics until today, including sabermetrics (at least the public ones), belong almost entirely to the second group, but are used as if they belonged to the first one. Para comprender rápidamente la diferencia, diremos que si todos los bateadores de una liga conectan 10 jonrones, 20 dobles y 50 sencillos en 240 veces al bate, todos sus datos darían exactamente lo mismo; To understand the difference really quickly, we’ll say that if all batters of a league hit 10 homers, 20 doubles and 50 singles in 240 At Bats, all calculations would give exactly the same results: AVG (.333), SLG (.542), wOBA(.372), BABIP(.304), etcétera. AVG (.333), SLG (.542), wOBA (.372), BABIP (.304), and so on. Por tanto, el análisis desde este punto de vista limitado, nos llevaría a elegir a cualquiera de ellos para un torneo de mayor dificultad, asumiendo que TODOS TIENEN LA MISMA CALIDAD. Therefore, the analysis from this limited point of view, would bring us to choose any of them, to play in a more difficult tournament, assuming that ALL OF THEM HAVE THE SAME QUALITY. Esto, sin embargo, no nos permite saber QUIÉN TIENE MAYORES PROBABILIDADES DE ÉXITO. This, however, doesn’t allow us to know which one has greater chance of success.

Selection Las estadísticas de selección por el contrario, nos darían números diferentes aunque todos tuvieran exactamente el mismo rendimiento.Stats, on the contrary, would give different numbers although all players had exactly the same performance. A veces incluso hacia extremos opuestos, como sucedió con nuestros jugadores modelo-inicial Abreu y Gurriel, y que nos permitió acertar en los pronósticos. Sometimes even to opposite ends, as happened with our initial model-players Abreu and Gurriel, and that allowed us to succeed in forecasts. Una estadística de selección es aquella que permite preveer el rendimiento en un torneo de mayor nivel, utilizando datos de un torneo menor.Selection stats are those that can predict the performance in a higher level tournament, using data from a minor one. Se enfoca también en el “Cómo”, y no solo en el “Qué”. It also focuses on "HOW" and not just on "WHAT". Su margen de error es visiblemente más pequeño que el de una estadística histórica. Its margin of error is visibly smaller than a historical statistic. Es el “Cómo” (las circunstancias en que se logra el resultado) lo que hace la diferencia. It’s the "HOW" (the circumstances in which the result is achieved) that makes the difference. La sabermetría actual se comporta PARCIALMENTE como estadística de selección, solo dentro de una misma liga. The current sabermetrics partially behaves as selection stats, just within the same league. Pero cuando se trata de pasar jugadores de una liga a otra (por ejemplo, de AAA a MLB), queda prácticamente ciega, como hemos ido demostrando. But when is necessary to move players from one league to another (for example, from AAA to MLB) they’re practically blind, as we’ve been demonstrating.

We’ve explained the Default and Average Syndromes in respective articles. AsDebido a que nuestro software ya está en fase de pruebas, hoy abundaremos en otros fundamentos del funcionamiento de Waibi .S our software is already in the testing phase, let’s abound in other WAIBI’s corner stone. Hemos explicado los Síndromes de Default y Promedio en respectivos artículos. Veamos en modo práctico lo que definimos como el Síndrome de Oportunismo.

THE EL SÍNDROME DE OPORTUNISMOOPPORTUNISM SYNDROME

AT WAIBI WORLD... THE GHOSTBUSTER SAID: “THE OPPORTUNISM SYNDROME IS LIKE A GHOST WHO LIVES WITH US AT HOME, BUT WE DON’T EVEN SUSPECT HE EXISTS. BUT ACTUALLY THE PROBLEM ISN’T THERE’S A GHOST, BUT HOW MUCH HE ACCHIEVES TO MANIPULATE OUR LIFE WITHOUT US KNOWING... I MEAN, MOST OF PLAYERS HAS SOME DEGREE OF OPPORTUNISM; THE POINT IS HOW MUCH THIS OPPORTUNISM CONTROLS THEIR PERFORMANCE...” 

La tabla muestra el rendimiento de varios bateadores cubanos, en su mayoría sluggers, en su última temporada eThe chart shows the performance of several Cuban batters, mostly sluggers, in their last season in Cuba.

WAIBI EXPECTATIONS

WAIBI SABERMETRICS SYSTEM AND YULIESKI GOURRIEL’S DRAMA.

Yulieski's Home run

Finally he’ve reached the Big Tent! So, all the speculations will end. The contract for 47 million between cuban player Yulieski Gourriel and Houston Astros already entered into force. The franchise has risen the player to class A, as part of his adaptation process to the Majors.

This particular case has been followed by Waibi since its begining, and it contributed greatly to change the way to understand the statistics for our analysis.

The opposite case, which was exposed in our first article in Baseball & Science section, is  José Abreu. In 2014, with cuban league data, we had predicted his great performance in the MLB, but in all honesty, we must confess that he positively exceeded our expectations. One of the reasons for this was the growth and technical specifies adaptation, made it by the player before starting in the big arena. His settings should not have been so many, and by improving small details, he “exploded” at best baseball in the world.

In the case of Yulieski, we don’t know how to date its technical condition is, as there’s not videos about his training. Technic was our big remark as a cause of his opportunistic performance. Gourriel starts at Class A, with balance of 3 hits in 4 at bats ( 0.750 AVG).  But the battered Cuban baseball is still a little above Class A. Currently could be considered at an intermediate point between AA and AAA. . So these figures indicate absolutely nothing new about whether the cubanito is suitable or not. There were a lot of early runs in the match, which is indicative of  low quality pitching.

We hope to see Yulieski finally shining in a top-level tournament, even though our forecast is he will not. We believe he’ll be a standard player with maximum flash in low and middle pressure games, unless his batting technique has improved enough in recent months, to acchieve to be succesfull when face pitchers with 96 miles average fastball, extensive repertoires, great control, and mainly, a very effective throws mixing. We say this because we found in analyzed data that Yulieski tended to yield very disproportionately against lower quality pitchers, unlike Abreu, which behavior was relatively and proportionately stable.

Abreu and Cepeda home runs

WAIBI DISSECTION

WAIBI SAID: HEY!! CHOOSE THE OPPORTUNE  PLAYER, NOT THE OPPORTUNISTIC ONE!!

ABOUT DATABASE ON BROOKSBASEBALL

As we have advanced in our previous articles, WAIBI Sabermetric System is focused on the qualitative evaluation of the players, not on their historical performance. For these reasons the system discards (or tries to dismiss as accurately as possible) the inconsistencies that arise in the application of the conventional notation system, in which we identified “Default” and “Average” as its fundamental syndromes. 

We have been developing the first phase of the system, but without possessing  a database that meets the necessary characteristics for our work, we have relied on other existing, which provide a fairly detailed data flow. So we've selected BrooksBaseball.

The choice has been justified in the distribution of data which can separate or identify the actions of each player progressively (whether batter or pitcher), so, we can delete multiple "default" data. Remember that even though “default” data doesn’t depend directly from Pitcher_vs_Hitter relation, it affects significantly the numbers of each player, distorting the decoding and final accuracy in the analysis. However, other "default" data distort the interpretation, (and therefore, the real idea about the quality of a specific player) could not be eliminated. This happens because the error is created directly from the annotation rules in force in the Official Baseball Rules.

For WAIBI, the distribution of each data (action) play by play  is indispensable. The essence of its functionality isn’t in the details of the phenomenon (as in most of the current sabermetric stats) but in the details of the pieces that make up the phenomenon, and how  intermesh one with the other. This makes it undoubtely more accurate to evaluate the quality of a specific player, even from this initial stage of implementation. The first Waibi rule is all about avoiding as much as possible what we recognize as Average Syndrome. However, the database implemented by BrooksBaseball, or at least the version we have accessed, contains a series of erroneous or incomplete informations that are hindering  the successful processing of all data. For example, the classification of "Des" and "PDes" columns (the first is the description of the final result of each “at-bat”, and the second is for each launch) often suggest absurdities in the conventionality of the game. For example,  “hits” situations, “extra-base hits”, “groundouts” and others, sometimes end in “called strikes” or “balls” instead of “In play no out” or similar. There’s some descriptions blank. “Hit by pitch” with “In play no out”. Even “Homers” are described 4 times as “foul”, in the latest throw of turn. There’re also “out” situations  recorded as hits in the conventional play by play score, just to mention some examples.

Despite this, BrooksBaseball is a complex data base, with a large number of interesting graphics. It contains invaluable information. We believe it’s an ideal site to assess specific players trends. This facilitates the characterization of them. In The Book, Tom Tango refers the illustrative concept of "La Famiglia". The charts and diagrams in BrooksBaseball allow us to interpret  this concept quite rightly, being able to discern by a deeper study, which are the fathers, brothers, children and grandchildren of each "Famiglia". In this way, the incongruity of some data, or even its absence, would not affect the interpretation, because of the enormous size of the sample, to evaluated  numerically something as inespecific as the trend. It's like the difference between a mortar and a rifle precision: both are great, the choice depends on the mission.

WAIBI DISSECTION

 WHY wOBA AFFECTS BEST HITTERS AND FAVORS WORSE? THE SYNDROME OF AVERAGE & THE WAIBI PULSE

 

This article references:

• Why "The Book" is a Sabermetric’s masterpiece and an excellent reference book? 
• wOBA operation step by step. 
• Sport’s aleatority vs. Random’s aleatority. 
• Use vs overuse of averages in Sabermetrics.  Error-In in wOBA averages, step by step. 
• What really measures the Runs Value of wOBA?  
• Brief introduction to the calculation of Waibi Pulse.

Here’s the offensive component of WAR, possibly the most used sabermetric stats of recent times. El WAR calcula el aporte integral de un jugador a su equipo en relación al promedio de la liga, incluido el performance defensivo. WAR calculates the integral contribution from each player to his team, in relation to the league average, including defensive performance.  

El wOBA es el Weighted On Base Average (Promedio de embasado ponderado), y su fórmula es wOBA is On Base Weighted Average, and its formula is:

 

Siendo NIBB, las bases por bolas no intencionales, HBP los golpeados por lanzamientos, y RBOE las bases alcanzadas por error.Being NIBB, the unintentional bases on balls; HBP, hit by pitch; and RBOE, the bases reached by error. Si la comparamos con el OPS (OBP + SLG), veremos que los parámetros son muy similares, salvo por la inclusión de los errores.When we compare it with the OPS (OBP + SLG), we see that the parameters are very similar, except for the inclusion of errors. Para el espectador común, la inquietud mayor aflora de los constantes numéricas que anteceden cada parámetro en la fórmula, resultado ingenioso de sus creadores, Tom Tango, Mitchel Lichtman y Andrew Dolphin. For the common viewer, the biggest concern surfaces because of constant numbers preceding each parameter in the formula, ingenious result of its creators, Tom Tango, Mitchel Lichtman and Andrew Dolphin. Apareció por vez primera en “ The Book : Playing the Percentages in Baseball”. It first appeared in "The Book: Playing the Percentages in Baseball".  

WAIBI DISSECTION (2)

The “Default Syndrome” introduces errors in AVG, SLG, RBI & RP interpretation.

This article refers among others:

• Introduction to manage AVG, SLG & RBI through Waibi System.
• Principles of Waibi's BQI & AVQ can detect the decrease of athletic performances before its noticeable appearance, and significantly improve the selection of athletes from minor leagues to higher-level tournaments.
• TOP TEN of harder and more affordable pitchers of 2015 season, until 21 May.
• The "Default Syndrome" in RBIs and Runs Produced.
• Positive Action vs Neutral Action in RBIs.
• How Scoring Rules in force, increaseng the margin of error in RBI interpretation?
• The third hitter: the most favored player by Circumstantial Statistics and  "Default Syndrome".

Average, Slugging, Runs Batting In & Runs Produced.

These are four of the more traditional parameters used by scouts, managers, statheads and fans, to evaluate the quality of a hitter. These are important to define not only the selectivity of the players, but also their order in the line up. However, these data contain large margins of error from the traditional system, to be used as selection stats.

SLG & AVG

Waibi analyzes these as Direct Parameters, because essentially they depend on the pitcher-batter relationship. The AVG was created (as ERA was) by Henry Chadwick, in the late nineteenth century, inspired by the cricket scoring system.T he Cricket’s formula related scored runs and  outs (like the quantity of innings for baseball ). Chadwick understood that scored runs depended on the team rather than on the individuality of the hitter, so he changed the model to the parameters that were on hitter’s responsability. So he decided to use the hits instead of scored runs, and the  bats instead of outs:

 

Although there’s a simple relationship between the positive actions of a batter, and the totality of possibilities to perform, these very revolutionary approach and analysis by Chadwick, became the foundation stone of offensive statistics. This same concept of extrapolation is applied broadly to create Waibi Sabermeric System.

The SLG ,meanwhile, is more illustrative and influential in developing the line ups, because describes in numbers, the extension of each hit, dividing them into four types: singles, doubles, triples and home runs.

However, none of them, describe numerically a serie of specificities that instead are taken into account when forming a line up or select an player. Let’s see again some of model situations that gave birth to Waibi  analysis: 

When Cuba formed his team for the third World Baseball Classic, once again they put the player Yulieski Gourriel as third in the lineup. This place is always reserved to the best hitter of the team. In reviewing his historical data in the Cuban League, greatly impress beyond the results, the rate  which he has been achieving, threatening several current records. However, an extremely influential important fact, has never been assessed for these analyzes: the low quality and parity of the current Cuban League. There are 16 teams in a country with less than 12 million inhabitants, where the massive presence in baseball has been greatly reduced by economic reasons. Cuban technical procedures which are out of date from several years. Besides, the strike zone is highly arbitrary, the tactical thinking of most of the pitchers is inefficient, as well as their control over the throws, and the variety of their repertoire; not to mention the focus on what happens on the field. Cuban League is highly entertaining, exciting and competitive, but technically very poor. This situation leads hitters to face  mostly low quality pitchers, which facilitates not only to achieve high performance stats, but allows them to be very effective with poor batting techniques, and mask the true quality of many of them.

WAIBI DISSECTION

ERA & WHIP: The peak of the Iceberg

This article refers among others:

• Introduction to basic principles of WAIBI SABERMETRIC SYSTEM’s formulas.
• Algorithmic and conceptual analysis of ERA. Updating formula step by step to PTRA.
• Many inconsistencies at Rules Scoring greatly distort the veracity of ERA.
• Ghost Runs & Circumstantial Runs, the best allies of ineffective pitchers.
• Algorithmic and conceptual analysis of WHIP. Updating formula step by step to PSIP.
• Semintentional Bases for Ball & Strike Out with Wild Pitch, "ghost" and "circumstantial" data respectively, in the calculation of WHIP.
• ERA & WHIP don’t supplemented. Comparative tables of the top 20 pitchers in MLB 2014 according to ERA, WHIP and PSIP.

One of biggest problems in Baseball stats, is that some traditional parameters (widely used yet) were designed to evaluate something, that its algorithmic structure fails to convey. Even though sometimes these leave out key variables, making the result confused and incomplete, its "practical simple use" and easily understood  by managers, coaches and fans, "legitimize" them as highly reliable. However, its conceptual weakness, VERY OFTEN shows many capable players that are not actually so capable, and others very efficients as if they weren’t.

This is the case, among others, of ERA and WHIP, two very useful  tools for decades, but now archaic to measure the pitchers effectiveness. Formerly there were more differences between  teams quality, and these inaccuracies didn’t take much weight. However, after the internationalization of strategies, tactics, systems and technical skills training of the best competitive level schools (mainly US and Japan), the quality’s contrast between players around the world has decreased greatly. Therefore, it is increasingly important to make a very careful  selection to aspire to victory. 

 ERA

Although many important sabermetric statheads have developed more functional variations of this formula, looking as close as possible to the truth, I consider that  the implementation of  ERA has more value as a general historical data, than as a selection’s tool. Its invention dates back to the late nineteenth century and is attributed to the English writer and historian Henry Chadwick, the founding father of Baseball statistics, who also created, among others, the first scoresheet, the scorebox, and AVG formula. In the times that we mean, there was not a statistical background in this sport, and Chadwick was inspired by the Cricket’s game stats, in which certain events are far away from Baseball complexities.

SCIENCE & BASEBALL

Baseball and Science. Background that have limited the development of full comprehension of Baseball. General details of the new BASEBALL WAIBI SABERMETRICS SYSTEM. 

This article references among others:

• From the first Curveball to Gyroball: Science raises the Baseball roof.
• Is really possible to create The Ultimate Player.
• The Cuban Yuliesky Gourriel: His seasons with the Havana' s Industriales could have been the catalyst for his international awakening. Its  technical deficiencies limit their success against higher quality pitchers. (Basic comparative diagram with other Cuban sluggers).
• Do the curveball "break" or is it just an optical illusion? Reasons to rub a ball.
• Introduction to the basic principles of WAIBI SABERMETRICS SYSTEM.
• The concept of WAIBI's Selectivity Index applied to Football, could shed light in the disputed Lionel Messi-Cristiano Ronaldo, to the best player of the decade.

Baseball is the only sport with a whole book of rules, and this is due to the amount of situations and variables that could occur in the ballgame. The baseball practitioner (player, manager, coach) generally ponders his knowledge, basing on thousands of game situations he has lived and resolved throughout his life. Therefore, he knows what is the intended purpose of each play experience, and what is the action to be executed before a small number of possibilities and variables. Usually, if he has brought success before, this invaluable experience makes him see statistics as a secondary weapon to win the match. Hence, despite the development of Sabermetrics, they continue to use it minimally, without a great effort to understand it thoroughly.

For his part, the statistic frequently is a thinker above average, with good academic grades and at least a standard mathematical understanding. However, statshead read the game usually  (never intentionally) as a combination of numbers and formulas to apply to general questions they have learned, often as public. Generally, the statshead doesn’t know what the reasoning of the athlete in each situation is, what he looks for, what goes through his mind. Thus, neither team have a deep knowledge about the other.

 The primary concept of college sports in the United States is the idea of an athlete-thinker, suggesting the conception of a man who can multiply his effectiveness by understanding both worlds. This type of symbiosis has been well tested at its finest by the incredible Shaolin monks, who linked extreme physical training, spiritual growth and philosophy in the same warrior; that’s why it is considered a martial art, not a sport.  Unfortunately, over time, the College Baseball didn’t fully realize their own concept, and have prioritized the pursuit of physical talent to the detriment of intellectual one, because to conquer sport tournaments in the category, is an effective and necessary way to promote and finance universities. Ironically, the same economic look at short term, has avoided that Baseball be a more profitable sport at long term.