2017’s Least Aggressive Base Stealing Teams

If you have not read my former post, 2017’s Most Aggressive Base Stealing Teams, I would recommend doing so before moving forward.

2017’s Most Aggressive Base Stealing Teams

Much like the previous post, I am beginning my look at the least aggressive teams with a high-level inspection of their aggregate stolen base rates ((SB+CS)/SBO)). These figures were downloaded from Baseball Reference. Light red represents one standard deviation below average and dark red represents two STDs.

Team Att per SBO Not Aggressive

Baltimore was dead last each of the past three years! On its own that would not be worrisome, but in conjunction with their notorious aversion for foreign signees, it becomes a concern. The below link to Baseball America from last July sums it up. They were the only team to abstain from acquiring a single player during last year’s J2 International Singing Period. It has to make you question why are their practices are so abnormal relative to the other teams. This is a red flag.

Baseball America 2017 J2 Singings

Not Agressive 3 yr with ranks

Seeing Baltimore dead last three years in a row made me curious how the other four teams fared in previous seasons. It turns out the Mets, Athletics, and Blue Jays were fairly docile each year. I think it’s fair to expect the trend to continue going forward.

The Phillies, however, were average to aggressive in 2015-2016. Was 2017 an outlier? It’s hard to say. I took a look at how often individual players were sent the past three years to see if anything could be gleaned from it. For some reason the Phillies started running less with Galvis, Hernandez, Herrera, and Altherr. Hernandez had a poor success rate (56.67%) in 2016 so I can understand why his attempts were reduced. In my cursory, unthorough (is this even a word??) internet searches I found Herrera and Altherr both had leg injuries last year. Altherr’s was a hamstring tweak in mid-July, and Herrera went to the DL with a hamstring strain retroactive to mid-August. Alterr’s injury was pretty minor and Herrera didn’t miss time until the last six weeks of the season so neither of these seem explain the large declines in their attempt rates. I am stumped.

PHI 2015-2017

Att.SBO vs SS Rank Not AggressiveTo learn more about the team-level stolen base attempt rates, let’s see how they compare to each team’s weighted average sprint speed. (SS Weighted Avs) Not too surprisingly, the teams that attempted the least steals on a rate basis also were among the slowest in the league. The exception was the Phillies who ranked 13th. This chart begs the question, which came first: the stolen base attempt rate or the sprint speed? I think the answer is the sprint speed. If teams do not put a high value on stealing bases it would probably start with the GM and players they chose to acquire. Looking at the four teams in question, this appears to be the case. Either way having slow guys on the roster is not going to incentivize teams to run.

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Let’s take a look at each team’s stolen base attempt rates at a player-level to see if anything can be learned from them.

Baltimore Orioles – Whether right or wrong, Dan Duquette constructed their roster with a total apathy for speed. Only four players exceeded the average sprint speed mark of 27 ft/sec, although this is a bit deceiving because Machado, Mancini, Schoop, and Tejada were right around average. As a team, their weighted average sprint speed was third slowest in the majors. Adam Jones and Tim Beckham were probably capable of running more but were rarely sent. As long as the current regime is at the helm, don’t expect Orioles acquisitions to get many (or any) attempts unless they are burners. But it looks like Baltimore is averse to acquiring guys like that in the first place.

Oakland Athletics – Only Rajai Davis and Marcus Semien were sent at above average rates. The rest of the team didn’t steal. I was a bit surprised to see Matt Chapman highlighted as a plus runner (1 STD > AVE). He’s never been known as a base-stealer. It makes you wonder if he has a slow first step but fast max speed. Overall, it’s pretty clear Oakland does not place a high value on steals, but if they have a speed-oriented guy like Davis they will send him.

New York Mets – Four runners were sent less often than their sprint speeds might imply. If you are an Amed Rosario fantasy owner should you be concerned? At first glance his stolen base rate looks to be in line with his sprint speed. I wanted to check to see how his SB Att% compared to similar runners league-wide (within .2 ft/sec) and did so below. His SB Att% was smack in the middle of the seven player sample so I think it’s fair to say the Mets weren’t curtailing his attempts. Still, the Mets were non-aggressive as a whole.

Amed Rosario Speed Comps

Toronto Blue Jays – Another front office that does not care about speed, in fact, they were the slowest roster based on my weighted average sprint speed sheet. Richard Urena was the only player well above average. It’s worth noting, however, they were willing to send above average runners at rates in line with their speeds, they just didn’t employ many of them.

In totality, theses tables were not terribly enlightening. but if we can learn anything from them, non-aggressive teams are not a death knell for fast runners. Prolific base stealers will get their stolen bases regardless of organization.

2017’s Most Aggressive Base Stealing Teams

In June of 2017, Baseball Savant introduced a new Statcast metric for public consumption. They defined sprint speed as “‘feet per second in a player’s fastest one-second window.’ The Major League average on a ‘max effort’ play is 27 ft/sec, and the max effort range is roughly from 23 ft/sec (poor) to 30 ft/sec (elite). A player must have at least 10 max effort runs to qualify for this leaderboard.”

Now that we have a year’s worth of data, and we are trapped in the middle of a cold, dark offseason, I thought it would be an opportune time to play around with it. Over the last few weeks I sliced, diced, and minced the numbers. One area explored was team sprint speed and its relationship to stolen base attempts. Sprint speed was pulled from the Baseball Savant Leaderboard and stolen base data was downloaded from Baseball Reference.

Team Att.SBO Plus

As a proxy for team aggressiveness I added team stolen bases to caught stealing and divided by stolen base opportunities (plate appearances through which a runner was on first or second with the next base open). SB Att% = (SB+CS)/SBO

The table above shows teams that sent runners most often in their SBOs each of the past three seasons. Light green represents one standard deviation above average and dark green denotes two standard deviations above average. One has to question how much these totals were the result of team aggressiveness and how much they were the result of team personnel. To check I took a weighted average of each player’s sprint speed by respective player’s plate appearances and then added to find team totals. SS=speed in the next table. If you’re curious to see the exact figures I attached my sheet here: SS Weighted Avs

Att.SBO vs SS Rank

Surprisingly, the 2017 Angels attempted steals at the highest rate despite being the second slowest team! The Brewers and Rangers were also quite aggressive despite their below average collective sprint speeds. The implication is these three teams had some sort of organizational edict to test defenses and attempt more steals. On the other hand, the Reds were the tenth fastest team. It’s also worth noting they were the only team to attempt steals at a plus rate (1 STD > AVE) each of the past three seasons.

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To make more sense of the team-level numbers, I decided to get granular and look at them on a player level. First, I wanted to see how many players on each team attempted steals at a higher rate than the league average (5.17%), which you can see in the first column of the table below. Then, I wanted to see how many “not fast” players on each team attempted steals more than the league average rate. I did this by introducing a second filter, player sprint speed. The second and third columns show how many players per team were non-elite & below average runners but attempted steals more than the average rate.

For the purpose of this post I defined above average sprint speed as greater than 27 ft/sec and less than 28.3 ft/second. In the sample of 451 players on Baseball Savant’s Sprint Speed Leaderboard, 28.3 ft/sec was one standard deviation greater than average and 27 ft/sec was average. The middle column below shows eight Rangers, six Angels, and six Brewers attempted steals at a greater than average rate while having above average or worse speed. In other words, eight Rangers, six Angles, and six Brewers were non-elite runners but stole at a rate greater than league average anyway.

# Plyrs per team with att per SBO over 5.17

And they were not alone! My efforts monkeying around with filters and subtotals in Excel yielded this table. What does it tell us? Well for one, the Reds were not as aggressive sending runners as their overall SB Att% would suggest. Additionally, you could argue the Red Sox and Diamondbacks belong in the same conversation as the Angels, Brewers, and Rangers. They were willing to send a comparable number of non-elite runners at greater than league average rates.

While useful, this table is imperfect. Notably, it fails to account for magnitude of the SB Att%. In other words, it treats players who barely clear the league average threshold the same as players who are well above average.

The screenshots below display SB Att% by player for the five aforementioned teams plus Cincinnati. I highlighted SB Att% and sprint speed columns to add some meaning to the figures. It is intended to help visualize whether players attempted steals at a rate commensurate with their speed. The darker the green, the further from the mean. For example, Ryan Braun’s sprint speed cell is white (below average), but his SB Att% is green (over a standard deviation greater than average) so he attempted more steals than his sprint speed would suggest.

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Color Coding Explained

For those curious I attached my full spreadsheet. There are two tabs which show SS vs SB Att%: One for a sample of 451 and another for a sample of 491. The second tab adds extra lines for players who played for multiple teams. Sprint Speed vs SB Att%


I made observations on each of the six teams from the slideshow in the blurbs below:

Arizona Diamondbacks – Micro-level, Ketel Marte only attempted steals in 3.85% of his SBOs, a below average rate, despite ranking 29/451 in sprint speed. He should see an increase in his stolen base attempts to at least the above average range next season. Macro-level, the team was less aggressive than I thought. They were the sixth fastest team on my weighted average sprint speed sheet. And they had five players who ran less than their sprint speed may imply. The large number of Diamondbacks attempting to steal at a high rate seems to be due to their roster construction rather than organizational tendencies. I.e. their roster was littered with above average sprint speed players.

Boston Red Sox – I would classify the Red Sox as somewhat aggressive. They were willing to send three above average runners, Benintendi, Betts, and Nunez at well above average rates. They were also “sneaky aggressive” in the sense they sent below average runners Vazquez and Chris Young at above average rates. They should consider sending Bogaerts more often. Not only does his speed indicate he is capable of it, he was only caught stealing once in 16 attempts (94% success rate).

Cincinnati Reds – How does a team with the fourth highest stolen base attempt rate (shown in first table) have only three runners attempt steals at an above average rate? Billy f****** Hamilton. That’s how. He attempted steals an absurd 33.33% of the time in his 216 stolen base opportunities last year. Simply silly. I want to know how many of his attempts included pitch outs. Billy (yes we are on a first name basis) and to a lesser degree Jose Peraza were complete anomalies on this list, and their rates heavily skewed the Reds data. Cincinnati as a whole is firmly in the non-aggressive bucket.

Los Angeles Angels – The Angels table was revealing. All of their eight fastest runners attempted more steals than their speed may suggest. None of their 11 slowest runners attempted more steals than expected. This helps to explain how they could simultaneously attempt the steals at the highest rate while having the second slowest roster. In essence, they were selectively aggressive, stealing more often than expected with above average runners and making little to no effort with below average runners. On a player level, I found it surprising that neither Cameron Maybin nor Ben Revere cleared the plus threshold for sprint speed (1 STD > AVE). However! (S.A.S. voice) They attempted steals at an elite rate (3 STD > AVE).

Milwaukee Brewers – This is a run happy team. Seven players ran more than expected based on their sprint speed, two of whom, Ryan Braun and Domingo Santana sported average or worse sprint speed. Jonathan Villar attempted steals three STDs over the league average rate with merely above average sprint speed.

Texas Rangers – Perhaps the most aggressive team in MLB, they sent some below average runners at above average rates, and they sent above average runners at well above average rates. Delino Deshields was their only real burner, but that didn’t stop them from sending eight other players at an above average rate, which was the second most in the league. (Shown in the second table, first column)

For fantasy players it would be wise to keep an eye on offseason acquisitions by the Rangers, Brewers, and Angels. These players are liable to see increase in their stolen bases in 2018. To what degree? This will be tackled in a future post. Also on tap, who were the least aggressive teams and what we can learn from their data?


A few notes on the information from the slide show:

*Many Detailed Statistics are based on play-by-play accounts accumulated by RetroSheet. These totals may be incomplete – (Copied verbatim from Baseball Reference). This is referring to the asterisks next to player names.

**No players in the sprint speed data set are greater than three standard deviations from the mean. That does not mean Buxton, Hamilton etc. are not 80 runners on the scouting scale. It just means the distribution for sprint speeds does not follow a normal curve.

***Not every player was available on the Sprint Speed Leaderboard, limiting our sample to 451, which is why the average SB Att% of 5.01% differs from the overall league at 5.17%.