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2018 Edwin Diaz - Top-10 Closer Performance?
Entering the 2018 season; Mariners skipper Scott Servais struck a wager with the teams budding 24-year-old reliever, Edwin Diaz. The stakes; a fresh hair cut for the manager, anchored around the youngster’s ability to record 50 saves during the regular season. Based off the title of this article you can imagine the outcome for the third-year manager. Now tied second all-time in Major League Baseball for saves in a season with 57; Diaz success in 2018 has helped put a core of younger players – alongside Mitch Haniger and Jean Segura – on the map in Seattle. Taking the mound, a total of 73 times; the Mariners won each outing in which Diaz appeared. Along with winning his wager; Diaz earned the Mariano Rivera American League Reliever of the Year award. Becoming the second youngest player to hold such a distinction, trumped by Washington Nationals Chad Cordero in 2005; whom took home the award in his age 23 season. Impressively; the National League equivalent of this award – named after Trevor Hoffman -- was won in 2018 by Milwaukee Brewers flamethrower Josh Hader, the third youngest player to win; being 16 days older than Diaz. We know about the saves; so, the question becomes, how good was Diaz this past season? Given the sheer number of qualified seasons from relief pitchers; multiple elimination processes took place in settling Diaz season into a relevant group; so to speak. We will review each step below as to be clear on how this data was mined. First Elimination – 1994 This was the year that Major League baseball adopted a wild card playoff system. Knowing that an impending playoff berth alters team management – to include the bullpen – and of the belief that modern day baseball should be handled, statistically, aside from historical baseball; the decision was made to pull data from 1994 on. Second Elimination - 2.20 ERA, 1.000 WHIP + 45 Saves Deciding on 45 saves took a bit of internal dialogue. Not wanting to use a number that would look out of place or inclusive of an odd-statistic; the decision not to limit the count at or below 40 revolves around the thought that under a quarter of a clubs games affected seems far from a dominant season. If you are not familiar with WHIP; it stands for Walks and Hits Per Inning Pitched. So, an even 1.000 would indicate that the given pitcher did not allow more than one hit or walk per inning over the given time period. This elimination got us 21 results: Before we move onto our third and final elimination; do you notice something about the 21 names above that doesn’t seem right from a traditional baseball sense? MLB’s single season save record, of 62 – in 2008 – by Francisco Rodriguez is not listed above. If you looked at the elimination data and thought 2.20 was an odd ERA to choose, my initial push to that number was to include K-Rods ‘famous season’. Upon bumping it a notch higher (2.30) and then altering the WHIP (1.300) as well, the data set returned was not of a manageable size to be contained in this article. Sorry Angels’ fans. This is, however, a great example of why this article exists. It’d be well and simple to write Diaz season off as the second greatest in relief history; but that wouldn’t be true, either. Third Elimination – RE24/boLI (or as I refer to it, a static statistic) Before I try – and hopefully not fail – to explain RE24/boLI; let me explain why I refer to this as a ‘static statistic’ and why I felt that was important in measuring relievers worth over a data set spanning seasons. WAR is becoming one of baseball most well-known statistics. It measures a player’s value in relevance to those around them in a given season; referred to as wins above replacement. In this scenario, the replacement is the average league player that season. Meaning if a player had two identical seasons; his WAR would be reported differently that second season unless all players recorded identical seasons. This is not a static statistic. Let’s jump into the first part of this statistic; RE24. RE24 stands for Run Expectancy for 24 Base-Out States. There are 24 different base combinations – also referred to as states -- that can be made in baseball. For instance; runner on first base, one out. That is a base state. Each comes with its own run expectancy or a static – there it is – number of how often a runner is given to score in its given state. This static number is built into it’s algorithm and is unchanged from season to season.Let's make this an interactive learning experience; below is an example of RE24 that all baseball fans can relate to. Say you are a fan of a fictional baseball team named the Cows. Your manager tends to push his starting pitchers a little further into their counts than he should and it leads to a reliever entering the game with runners on first and second, no outs. Note, this is not a reliever that you personally believe should be playing on the Cows; he’s a bum, for lack of better words. This reliever comes in and immediately elicits a double down the line; scoring two runs. You, an armchair manager, throw your arms into the air; however, for the remainder of the inning he is flawless. Here is where RE24 becomes a factor. Our relievers box score does not show that he threw the pitch during the play in which both those runs crossed the plate. In our scenario, that designation is left for the starting pitcher. Major League Baseball views those runners as inherited and credits them to the pitcher that put them on base. Lucky for you, RE24 has a plan to punish that bum reliever. Each state or combination – as we discussed before – has a static expectation of how many runs are to cross the plate. In our reliever’s situation, above – runners on first and second, no outs -- expectation is that 1.437 runs will score that inning. Knowing that while he was on the mound, two runs were allowed, the data can be used to process that relievers RE24 as: The Expectation (+1.437) – The Actual (+2.000, for the 2 runs) which equals a negative balance of –0.563 for the inning. Now onto the second part; boLI. boLI stands for Base Out Leverage Index. While RE24 assigns an expectation of runs allowed off a given base out state; boLI measures the leverage index of those states. For instance, when mixed with RE24, our reliever – from the Cows above – would be relieved partially of his -0.563 statistic from his inning pitched when divided by the base out leverage that comes along with runners on first and second, no outs. Leverage Index has plenty of non-believers on the internet but it’s necessary nowhere more in baseball than it is for measuring the worth of relief pitching. Rarely does a reliever face the lead-off batter out the gate or enter with no runners on base. Also, they are less likely to see a platoon advantage and face more pinch hitters than starting pitchers due to late inning substitutions.Finally, the data set. From the graph, above, you can see that Edwin Diaz ranks the tenth highest RE24/boLI statistic. I should note that when I selected 1994 as the beginning of our data set; statistics were not yet pulled and Diaz selection as number ten is purely coincidence. This graph is topped by none other than Eric Gagne’s Cy-Young season in 2003. It should be noted that we have one duplicate season on this list; and that was the battle for 2004 National League Reliever of the Year. Armando Benitez would finish second to Eric Gagne in voting. This is another great example of RE24/boLI at work in the relieving universe. Benitez registered an ERA that is nearly a full point lower and holds two additional saves on Gagne; yet the latter would win the award. While both have similar statistics in RE24/boLI; the edge is clearly that for the Dodgers counterpart, above. Let’s get outside the statistics and examine Diaz season closer. What is the number one thing you ask of your closer on the mound? While the majority of you are answering strike-out(s) – and you are not necessarily wrong – keep in mind it takes three pitches, at minimum, to register a strikeout. So, on the pitches that do not constitute a punch-out, you are looking to keep the ball in the pitcher’s hand; not in the field. This statistic – as shown above – is known as IP% or In Play. You can see in our graph that Edwin Diaz and Cy-Young Eric Gagne – to determine which season we are describing – finished their campaigns with an even mark; 46%. This dominance is no better described than by noting that this result is over 25% lower than that of 1999 Mariano Rivera, also seen in the graph above. Closers often keep the ball in their hand – annually registering a higher percentage than starting-pitchers and middle relievers – by maximizing a statistic called S/STR% or the measurement of total strikes swung at. Note that Edwin Diaz registers second in this measurement – which, undoubtedly led to many of his 124 strikeouts in 2018 -- sandwiched by two of Eric Gagne’s finest seasons in Los Angeles. Diaz brand of power pitching was characterized no better last season than by fellow teammate, and pitcher, James Paxton: “It’s 97-98, but it jumps. There is different kinds of 97-98. There is the ones that the hitters call a light fastball, whether it’s straight or (because it) doesn’t hold its velocity well. Edwin’s got one of those balls that’s a heavy 98; it holds its velocity well and it’s got movement and life at the end of it, and it just makes them extremely hard to hit. Finally, from the graphic above, let’s look at aLI. This stands for Average Leverage Index. For a pitcher, aLI is the measurement of difficulty for the average batter faced over a given time period. In our graph above, this time period is the season as marked. Note that no other pitcher holds a higher mark than Diaz. Whether you list his performance as the tenth best of the above data set; or whether you list him higher, due to our discussed closer relevant statistics in the graph above, it’s clear that no one should be wagering against him next season. Disagree; think one of the seasons I eliminated – or even 2008 Francisco Rodriguez deserves to be on this list? Let me know in the comments.
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