There are a few articles, including one by Travis Sawchik, arguing that tie games might not be as bad for baseball as you think. The truth is that not only would ties have had no impact on who reached the postseason in 2018, but they would have shaved off four minutes from the average time time.

Using regular expression to parse box score data from RetroSheet, I’ve looked at how the 2018 season would’ve been different without extra innings. Here’s a look at the postseason standings as they were compared to how they would’ve looked with ties (scored 3 points for a W, 1 point for a T, and 0 for a L):

With ties, the 2018 postseason still has the same cast of characters, although the Dodgers and the Rockies would have swapped places in the NL West, causing the Dodgers to go to the Wild Card game.

That’s only looking at 2018. When examining the past five seasons, I found that the postseason implications of tie games would be pretty minimal.

In the plot below, each point represents one team’s season. The X-axis is the number of games that would end in ties and the Y-axis is the number of places a team would’ve moved in their division.

For simplicity, I’m defining postseason implications (PS Implications) as a team missing or making a Division No. 1 or Wild Card No. 1 or No. 2 with the scoring system described above.

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The infield shift is a much-maligned defensive strategy, hounded as one of the worst analytics-based changes to baseball. Multiple times each season there will be some conversation about banning the shift, and each time pros, ex-players/managers, commentators, and analysts will chip in with their two cents. But for now, the shift is here, and it is as popular (with the fielding teams) as it has ever been. Just under 26% of all pitches were thrown with some form of infield shift in place in 2018, 22% of at-bats had a shift for the entirety of it, and 30% had at least one pitch shifted.

As you can see, left-handed hitters are far more likely to be shifted than their right-handed counterparts, with 46% of left-handed ABs seeing a shifted pitch versus 19% for righties. This makes rudimentary sense as the shifted players for a left-hander are closer to first base, so they have a greater chance of impacting the play to first and therefore stopping a potential single.

I have taken players who have 100-plus at-bats in 2018 both against a shifted and non-shifted infield, then I compared the outcomes. There were 132 such players, and their combined number of at-bats was 72,389 (39% of the seasons total). I have split these up into four categories based on the handedness of the batter and the pitcher.

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Andrew Miller has an undeniably nasty slider. As a Red Sox fan, I remember it far too well from the 2016 postseason. Big Papi’s farewell tour didn’t seem all that fair when you consider the way the Red Sox ran into the buzz-saw that was Miller and the Cleveland Indians. Sure, I’m grateful for Miller helping the 2013 version of the Red Sox win a third world title since 2004, but come on Andrew, you had to ruin Papi’s goodbye?

With Miller’s recent signing with the St. Louis Cardinals, I found myself exploring his FanGraphs page. I stumbled upon this article, Andrew Miller on the Evolution of his Slider, and I instantly began to wonder if pitchers had similar experiences developing their sliders in the 2018 season. The first step in this analysis was to evaluate the evolution of Miller’s slider.

What jumps off the page is the change in velocity. Miller saw a 4.6 mph increase in his slider from 2011 to 2012, then another 3 mph added from 2012 to 2013. This in large part had to do with Miller moving from a starting role to a relief role during his time with the Red Sox. Given that information, however, an increase in velocity that drastic not only shows a pitcher’s willingness to adapt, but also a pitcher’s ability to adapt. By observing Miller’s slider splits, we see that ability to adapt almost immediately.

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When Statcast was launched, we were graced with incredible new stats such as Exit Velocity and Launch Angle, which revolutionized how we evaluate hitting. This new information confirmed obvious things like that Giancarlo Stanton hits missiles, but it also gave us a new breed of hitter. Daniel Murphy, Justin Turner, J.D. Martinez, and others looked at the data and made adjustments that started maximizing their power outputs. The standard evaluation method has become to look at EVs mixed with LAs to determine who is one tweak away from stardom. Hitting is a complex beast, with pitchers throwing 95-plus with nasty hooks to go with shifting defenses. Ultimately, a hitter is looking to produce solid contact regardless of where the ball goes. The goal of this analysis is to identify hitters who have an inefficient spray chart and see how they could optimize their profile by hitting more balls in a different direction to maximize production. Luckily with Statcast, we can now try to find these answers.

To do this analysis, I used Baseball Savant to gather 2018 Exit Velocity and xwOBA to Pull Side, Straight Away, and Oppo Side for all hitters with at least 50 plate appearances. I then used FanGraphs to pull the 2018 data for Pull%, Mid%, and Oppo% to discern how often a hitter attacks that field. I used 50 PAs as a filter since this is about where exit velocities become stable and helps weed out pitchers and other noise. This does create gaps in the data because some players didn’t register 50 PAs of a batted-ball direction. This dataset gives us the ability to look at how hard a hitter hits the ball to a field, what was their expected damage (xwOBA) to that field, and how often they went that way.

The first category I looked at was players who could use the opposite field more often. To do this, I looked at players who had an above average Oppo Side xwOBA and a below-average Oppo%. I used exit velocities to each field as a proxy to justify the directional swing change. Read the rest of this entry »

With so many complex statistics out there, I wondered if there was an easier way to project winning percentage or runs, a way that is simple yet more complex than Bill James’ classic Pythagorean Win Expectancy. To create a statistic like that, I would have to create one comprehensive stat for offense and one for pitching. Ultimately, I came up with the following and named them “Run Value” and “Pitching Run Value,” respectively.

RVAL = ( ( TB + BB – SO )/4) + RBI + HR  

PRVAL = ( ( ( H + BB – SO )/4 ) + HR) x FIP

These two metrics are used for teams. In the batting RVal formula, the higher the better. I tried to get down to the pure number of runs that a player or team produces by using the very relaxed definition of a run being four bases. In the pitching PRVal formula, the lower the better. I did something very similar to the batting stat by trying to get the pure run total. I then put the two stats into the win expectancy formula:

RVALWinExp = RVal^1.83 / ( RVal^1.83 + PRVal^1.83)

I then ran a program in R to see how closely this stat correlates to actual team win percentage for all teams from the 1998 season through the 2018 season. In addition, I tested to see how Bill James’ win expectancy formula correlates to team win percentage over the same period of time. The results are below. Read the rest of this entry »

Note: This is a piece I have blogged about previously for a British baseball site located here, and this is a slightly updated version.

Jeff Sullivan does pieces on the worst called balls and strikes at the halfway mark and end of each season. These are usually quite bizarre calls that have some unusual circumstances behind them, but for the most part they don’t have too much influence on the game. However, in this postseason, there was a poor “strike” call which had a huge impact on a game.

In bottom of the second inning of Game Three in the NLDS series between the Braves and the Dodgers, Walker Buehler was in a difficult situation with two outs and runners on second and third after an error from Cody Bellinger. The Dodgers decided to intentionally walk Charlie Culberson, loading the bases, to get to Braves pitcher Sean Newcomb – a fairly standard approach in the NL. But Buehler fired four balls to Newcomb and walked in the first run of the game, bringing up Ronald Acuna, who Buehler threw another three balls to to end up down 3-0 in the count.

Then came “ball four,” but it wasn’t called a ball despite being two inches above the top of the zone, as home plate umpire Gary Cederstrom called a strike. That meant Buehler threw another pitch to Acuna, who launched it for a grand slam, resulting in a score of 5-0 and not 2-0. The potentially “pitcher friendly” call by the umpire cost the Dodgers three runs in a game they ended up losing by just one.

To go to a hyperbolic extent, this meant they lost the game, they then had to play a further game in the series against Atlanta, they were then more tired than the Brewers in what became a seven-game series, they were then more tired than the Red Sox, and they therefore lost the World Series. Certainly a stretch, but it’s not hard to see the effect in the game considering the Braves managed just three runs in the other 35 innings of their four-game series.

Not every mistake made by an umpire has an easily identifiable ramification like that, but they do happen in most game, and it is no surprise that MLB and the WUA (World Umpire Association) want to have the smallest number of mistakes possible. Nowadays they can do this by looking at how many calls an umpire got right or wrong thanks to systems that track the speeds and trajectories of pitched baseballs. Read the rest of this entry »

Despite the generally slow free agent market and the continuing increase of bullpen usage, starting pitchers have done fairly well for themselves this winter. Patrick Corbin inked a nine-figure deal, blowing past most projections to get a guaranteed $140 million. The Rays shelled out their largest free agent contract ever, giving Charlie Morton $30 million over two years. Nathan Eovaldi parlayed a strong second half and postseason heroics into a four-year, $67.5 million pact to return to Boston, and J.A. Happ got half that from the Yankees for his age 36 and 37 seasons. Even past-their-prime options such as Lance Lynn, Anibal Sanchez, and Matt Harvey were given eight figures, the former two on multi-year guarantees.

Yet arguably the most accomplished hurler among this year’s crop of free agent starters remains unsigned – Dallas Keuchel. FanGraphs’ Crowd Source and MLB Trade Rumors projections both had the 2015 Cy Young winner in the neighborhood of four years and $80 million, which would exceed Eovaldi’s deal for the second-highest guarantee among starters.

Of available starters, Keuchel was worth the second-most WAR last year (3.6, behind only Corbin’s 6.3), and projects to be the second-most valuable next year (3.3 WAR, just behind Corbin’s 3.5). Much has been made of his decline in punchouts (his strikeout rate dipped to 17.5% in 2018, fourth-lowest among qualified pitchers), but his velocity has remained steady and he’s continued to limit both walks and homers while inducing lots of ground balls. In 2018, Keuchel topped 200 innings for the third time in five seasons, and he’s been an above-average starter in all of those years.

At 31, he’s not young, but he’s younger than Happ (36), Morton (35), Sanchez (35), and Lynn (32), all of whom received multi-year deals. It’s fair to say that Keuchel doesn’t have the upside of Corbin or Eovaldi (or maybe even that of Morton or Yusei Kikuchi), but his consistency and track record should appeal to plenty of teams in need of a rotation upgrade.

Happ, a southpaw with a similar reputation for durability and above-average-but-not-elite performance, and Keuchel have been almost identical over the past three years (518 innings and 9.1 fWAR for Happ, 518.1 and 8.6 for Keuchel). But Happ is four years older, so over the course of his next contract, Keuchel’s output could quite reasonably look a lot like Happ’s recent past – that is, a 170-inning, 3-win metronome.

However, there seems to be some concern or trepidation surrounding Keuchel, a pitcher whose raw stuff was never overpowering, and the sustainability of his results. And looking at some of his underlying metrics, it’s easy to see why. Read the rest of this entry »

Managers are always attempting to optimize their lineups for success. Whether they make in-game decisions like double-switches and lefty-righty matchups, or choose to change things up based on recent or historical performances, every move is meant to give their team the competitive advantage. What if they also made alterations based on pitcher groupings? In this article, I will attempt to determine if batter performance is impacted by pitcher clusters that are organized by pitch speed and pitch proportion.

The parameters used to cluster pitchers are below:

• Proportion of Pitch Thrown
• Average Pitch Speed

These statistics were calculated for the following pitch types:

• Changeup
• Curveball
• Eephus
• Cutter
• Four-seam fastball
• Sinker
• Two-seam fastball
• Knuckle-curve
• Knuckleball
• Slider
• Splitter

*All data in this study is from 2010-July 2017 (MLB Gameday). Read the rest of this entry »

Since opening in 1999, Coors Field has provided the most offense-friendly environment in baseball. Despite the inherent volatility in park factors for single-season data, Coors has “won” the park factor title in 15 of the past 20 years, never finishing lower than third. The dramatic increase in home runs may be the most striking effect of the thin air about a mile above sea level, but all balls in flight, including pitches, are affected. Due to the lower air density, the spin-induced movement of a pitch thrown at high altitude will be lower than that of a comparable pitch closer to sea level.

Check out the average movement on Adam Ottavino’s pitches in 2017 and 2018 separated by home (purple) and away (black).

You may recall Ottavino said recently that he is confident Babe Ruth couldn’t hit any of this stuff. Read the rest of this entry »