A Look at Statcast’s Broadcast Debut

Last night’s broadcast of the Cardinals and Nationals game debuted live, in-game Statcast enhanced graphics and replays. Statcast is the next-generation player tracking technology that combines both optical and radar measurements promising to create new ways to quantify previously unmeasurable aspects of baseball. The hype leading up to this game was billed as historic, and here at FanGraphs, we even had a special edition of the After Dark Live chat to cover this momentous occasion.

If you were expecting something earth-shattering from Statcast, once you began to watch the game you were probably disappointed at the slow start. If you were unable to watch the broadcast, no need to worry, because all the important replays from the broadcast were posted on Major League Baseball’s site, and I’m about to review and critique the different elements of the Statcast presentation.

First, before analyzing specific images and gifs from the game, MLB Network appeared to treat this as a normal broadcast using Statcast to augment their broadcast, not define it. 90% of the broadcast contained traditional camera angles, graphics, replays, and other broadcast elements. When Statcast was used, it was to produce enhanced replays and player positioning. There weren’t graphical overlays over live-game action aside from a few pre-pitch positioning graphics. ESPN currently has more detailed graphics for live-action pitch tracking with their K-Zone graphical overlay.

The Statcast enhancements can be broken down to two general categories: live player tracking and replay play tracking. The live player tracking can highlight where a player is, who he is and how far away he is from a certain point, like a base. Replay play tracking entails more detailed metrics and included ball tracking. All of these additional graphics came from a limited number of high-angle cameras, and not the full arsenal of broadcast cameras. The high cameras were most likely the only cameras calibrated to work with the Statcast data to display the graphics correctly.

The most constant feature of the Statcast enhancements to the broadcast was Shift Trax, which was a graphic that showed the positioning of each defensive player. It’s similar to the feature that baseball game videogames have had for at least a decade. From what I could tell, it didn’t update during game action to show the movement of the players.

Statcast_Shift

From the same high-angle camera, MLBN was able to highlight the players on the field with graphics. In this case, it was just the identification of the batter and the pitcher.

Statcast_Label

Throughout the game the broadcast would cut to a shot of a base runner with a lead and denote how large the lead was.

Statcast_Lead

The most interesting aspect of the lead display is that the distance is measured along the line between second and third base and not directly between the runner and second base. This was most likely done to show the lead the runner has taken toward third rather than away from second. However, this would not be completely accurate since the runner now has a longer distance than 76 feet (90-14) to get to third. It might be ideal to show both the distance from the bag and along the baseline, but that might be data overload, where we are given too much data to derive any meaning from the presentation in any appreciable amount of time. This is pre-pitch coverage, not a math problem.

Speaking of data overload, we had a good discussion about this Danny Espinosa bunt play in our Statcast chat.

Espinosa_Bunt

As with all Statcast replays that clocked a runner going to first, everyone said they wanted to see the batter’s time from contact to first and, ideally, a time for defensive play as well. The player’s ‘instantaneous’ and max speed are a nice engineering feat, but those metrics don’t tell the story of the play as well as time from contact to reaching first base.

While having different measurements would be ideal, the player trail graphics are a great visualization of the movement on the play. The faster the player runs the redder the trail becomes. You can see how Espinosa accelerated to first, and when Kolten Wong hit his maximum speed before recording the out.

The next two gifs show a fantastic diving catch by Jon Jay and Yunel Escobar’s walk-off home run. Once again, we have a wide, high-angle camera shot with detailed information about Jay’s route distance and running speed and Escobar’s batted-ball trajectory; this is really cool information. The only criticism I have here is that the green trajectory doesn’t match ball on camera, and it appears to be displaying longer than what actually happened. This is most likely a calibration issue with the cameras, or the trajectory is showing a calculated, projected path rather than a tracked path.

Jay_L8

Escobar_HR2

Throughout the broadcast there were a handful of Statcast-enhanced replays that playing tracking with a selection of summary stats describing the play. For the most part these stats appeared to be a preselected set of data for different types of plays. I would conjecture that as this technology matures metrics more poignant to each particular play’s story will be displayed. For example, Jon Jay’s diving catch in the bottom of the ninth had a first step time of 0.3 seconds. That’s an important piece of information to understand how Jay was able to get to the ball and prevent runs from being scored.

Finally, I think the enhanced pitching metrics will be the first new stats coming out of the Statcast data to be widely adopted. With Trackman radar readings instead of PITCHF/x’s high-speed cameras, you can calculate the spin on a particular pitch, measured in RPMs, which can give us more information about the effectiveness of different types of pitches in a way that isn’t obvious from watching it on TV. Likewise, the radar-based system can measure perceived velocity, calculated using the pitcher’s individual extension to ‘normalize’ the pitch relative to an unknown league average. Pitchers with shorter extensions will have a lower perceived velocity than pitchers with longer extensions each throwing with the same actual velocity. These might be more upgrades over PITCHF/x than a revolution like the fielding data could be, but these are more actionable pieces of information out of the gates.

Harper_K

While I spent a lot of time in this post discussing ways to improve Statcast, I really enjoyed the broadcast last night. I think this technology will greatly enhance MLB broadcasts, and the data it produces will provide teams and (hopefully) public analysts a treasure trove of metrics and new insights. During the chat many people were asking about whether or not the data would be made public. Each game’s raw data requires a lot of storage space, so distribution logistics alone will probably prohibit MLB from releasing that data to the public. Add that to the fact that teams don’t want to lose any competitive advantages that data might provide. However, I think MLB will distribute aggregated data on their website or attach some data to their current Gameday application like what they are currently doing with batted ball data.

We hoped you liked reading A Look at Statcast’s Broadcast Debut by Sean Dolinar!

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I code a bunch of things here. I really need to update my blog about statistics at stats.seandolinar.com.

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RMR
Guest

It was a fascinating proof of concept. What remains to be seen is what metrics and visuals will prove truly informative rather than merely interesting. As a fan watching a game, I’m not sure what knowing what a runner’s top speed does for me. What question does it answer? What insight does it provide? Most of the data presented lacked useful context. Now, if you can tell me that a 0.3 second reaction time is objective good or bad or that Espinosa accellerated slowly out of the box and would have reached 1st had he been as fast as average, then you’ll have something the fan can sink his teeth into.

It’s an awesome show of technical achievement and preview of what’s possible. But as an enhancement to the broadcast, it’s not quite there yet.

Well-Beered Englishman
Guest
Well-Beered Englishman

I agree. I think part of the problem is the broadcasters – really only a very few broadcasters are ready to converse about this type of thing in an insightful way (the Rays, Cubs, and Mets guys are all I can think of).

Last night’s clips, from what I’ve seen, mostly boiled down to:
“And let’s see what StatCast says… StatCast says he ran 14 feet to catch that ball. Cool.” Honestly, I was underwhelmed, because without anybody to tell me if, e.g., Ian Desmond’s 0.2 second first step is especially good/noteworthy, it’s just another number to me.

wildcard09
Member
Member

What makes you say the Rays broadcast guys are ready to talk about statcast in a positive way? I was forced to listen to them for last night’s Sox vs Rays game, and I thought listening to them was a nightmare. They seemed to repeat themselves every single at-bat and didn’t seem to know small-sample-size is a thing. Granted, that was the first time I’ve listened to them (small-sample-size irony?) so maybe it was a bad night, just curious why you think they’re qualified.

Well-Beered Englishman
Guest
Well-Beered Englishman

Maybe I was wrong. My comments were based on some broadcasts last spring, so it has been a year.

wildcard09
Member
Member

Yeah I wish I remembered their names. I’ll be watching again tonight though, so let’s hope for a bit of a better broadcast.

Moonshadow
Guest
Moonshadow

While the Mets broadcast team is terrific, they have taken to advanced metrics very grudgingly.

fothead
Guest
fothead

Actually, believe it or not, the Yanks broadcast team may handle this info well based on who’s in the booth. No way O’Neill cares about this stuff (still love ya Paulie) but David Cone, John Flaherty and Al Leiter would do a fine job I believe. They’ve come along way both in the front office, pn the field and even conditioning the fanbase.

Houston’s team seems really good too. Loved listening to them during MLB Extra Innings free preview vs Cleveland. Also props to Milwaukee’s team as well from the times Ive heard.

The Duda Abides
Guest
The Duda Abides

There is nothing that the yankee broadcast does well.

rex manning day
Guest
rex manning day

The in-game visuals remind me a bit of that time a while back when NHL games would add a red tail to the puck when it went over 100mph. It’s kind of nifty-looking, but not actually all that informative or helpful.

The data this stuff produces should be extremely interesting, and hopefully as everyone gets better at analyzing all this information they’ll be able to produce more insightful graphics. But I suspect that for a while at least, the in-game stuff will just be a slightly interesting novelty.

Also, I think the most interesting possibilities don’t lend themselves to quick visualization. For example, on that bunt, the runner’s time would be handy, but I think the more interesting thing is how the defenders move. The 1B runs up, fields, turns, and throws, and in that time the 2B runs much faster to go cover first, catches, and tags the bag all in time, and in the meantime the pitcher (who could have fielded the ball but didn’t; did he have an inferior angle? or was it just that the fielder gets first call at the ball?) goes from running toward the ball to backing up first. That’s a lot of moving parts, and everyone needs to move efficiently to get it done in time, and I think there’s probably a fascinating data nugget buried in there somewhere that you can’t dig up 15 seconds after the play is over.

Or take Jay’s catch. The first step is interesting, but what about his placement? How far was he from his average starting position? From the league average position against this batter? Would the league average starting position have had an easier or harder time making that play? Etc etc.

All of which is to say: hopefully as this technology develops, they’ll be willing to revisit replays 5, 10, 15 minutes after they’re over, once an analyst has had time to sort through things. The in-game graphics will never be as interesting or informative as the deeper analysis of the data, but given it’ll hopefully get at least a little bit better.

wildcard09
Member
Member

Speaking of the puck-tail, I can’t remember for sure what network it was, but do you remember the broadcast that used to do the puck “shadow” to try and highlight at all times where the puck was? That was atrocious and pretty much ruined watching the games for me, so glad they got rid of that.

FuriousToaster
Guest
FuriousToaster

You can thank FOX for that gem IIRC. I liked the ability to see where the puck was through the boards, but every other aspect of the implementation was crap. (OK, the highlight when a shot passed a certain threshold was cool too).

Pirates Hurdles
Guest
Pirates Hurdles

Ahh the glow puck, those where the days. Fox was trying to make the viewing experience better in the non-HD TV days. Back then it was a lot harder to see the puck on TV than at games in person. The idea was to grow the game by adding casual fans who would not have been interested previously due to difficulty following the play on TV.

wildcard09
Member
Member

Thanks, I was thinking it was FOX but couldn’t be sure. Agreed that it could have been a nice addition in limited use, but definitely not full time like they had it.

BMarkham
Guest
BMarkham

With more games comes averages for all these new stats, and that will provide context.