Baseball analytics have long been focused on helping teams gain a competitive advantage by finding players their competitors have overlooked for one reason or another. While this started by using very basic statistics such as on-base percentage, recent decades have seen the methods for doing this become increasingly advanced. Major League teams are now pouring... Continue Reading →
Usually when a pitcher adds more spin to his fastball you see a relatively similar increase in velocity; however, these 5 pitchers go against this pattern drastically. I looked at 5 pitchers’ fastballs who have lost 100 or more RPM this year compared to their 2020 data, while also showing an increase in velocity. These... Continue Reading →
In his abbreviated rookie year, James Karinchak dominated out of the Cleveland Indians’ bullpen. He was a little wild, but striking out 48.6% of batters more than made up for it. The root of his success? One of the best four-seam fastballs in the game. His combination of velocity and backspin translated to his fastball... Continue Reading →
Coors Field has long been synonymous with offensive slugfests which give pitchers nightmares. In addition to this, we know that the higher altitude in Denver is what affects the baseballs' trajectory as they are both thrown and hit. Often the "thin air" is brought up as what allows the baseballs to travel further in Coors... Continue Reading →
If you are unfamiliar with the term "Win Score" please check out the: Introducing "Win Score" article. The equation for Win Score as stated in that article is: Win Score = [(League average runs-(ERA+FIP)/2)*5 + .75(innings) + .5(strikeouts)]/Games Pitched. The point of Win Score is to replace the current "win" statistic as it is outdated... Continue Reading →
Pitchers in Group 1 have a record of 0-10 with a 2.69 (ERA+FIP/2) vs. pitchers in Group 2, with a record of 7-0 with a 5.08 (ERA+FIP/2)... Historically, the “Win” has been a steady metric that has been simple to understand and commonly used to judge how good a pitcher was. Over time we have... Continue Reading →
In the field of statistics, we use the the law of large numbers to explain that as the sample size of something increases, the average of that sample will fall closer to the mean of the population as a whole. The proof of this law has existed since the publishing of the book Ars Conjectandi... Continue Reading →
While baseball has always been a game of numbers, the increase in large datasets and the availability of these datasets over recent years has greatly facilitated the ability of both public analysts and teams to draw conclusions and insights into the game that they previously would not have been able to. As the data has... Continue Reading →
We have long been aware of the fact that baseballs spin when traveling through the air and that Major League Pitchers are able to generate quite a lot of spin when they release their pitches (average spin rate on Major League pitches was around 2270 RPM’s last year). It has been common knowledge for a... Continue Reading →
Opening night was quite disappointing as a Nationals fan. Our collection of neighbors sat distanced and outdoors, Soto was out due to a false positive, and to top it off the skies opened up sending us scrambling. It was the first of many losses in 2020. As the last few stragglers left the gathering, I... Continue Reading →
Even though I spend ~95% of my life thinking about baseball related questions, one was recently posed to me that I had never previously given much, or any, thought to. Since hitters will likely lose exit velocity when hitting the ball to the opposite field, how big of an advantage would being able to maintain... Continue Reading →
This is part 2 of a series where an attempt will be made to better understand the effects of the increased number of shifts is having on overall offensive production across Major League Baseball. While Part 1 considered the impact on the league as a whole, this part will take a individualized look at certain... Continue Reading →
As analytics departments across Major League Baseball have gotten access to better tracking system data over the past decade, the use of the shift has skyrocketed across the league. The idea of the shift is that by positioning fielders where batters most frequently hit their balls in play, the fielding teams can attempt to limit... Continue Reading →
In part 1 of this series, a macro level attempt was made to understand what causes the Time Through the Order Penalty (TTOP). While there was some interesting information uncovered in part 1, taking a more individual player focused approach in this part will likely yield better results. Most baseball followers are likely familiar with... Continue Reading →
This is part 1 in a series where I will attempt to better understand what causes the Times Through the Order Penalty. Part 2 of this series will take focus on individual pitchers in an attempt to better understand the causes of the TTOP. If you've spent time watching baseball you are probably at least... Continue Reading →
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