Blocking Profiles - Part 2
Stuffmasters
Related:
Serving Profiles
Digging Profiles
Articles in this series:
Part 1 - Introducing The Data
Part 2 - Stuffmasters
Part 3 - The Funnelers
Part 4 - Touchy-Feely
Stuffmasters
Here’s the ten teams who most closely fit the profile of being relatively better at stuff-blocking the ball than other aspects of blocking.
Stuffing the ball tends to be the main measure by which blocking is judged, so this is a bit of a “no duh” profile. But, there are some other aspects of blocking. The ability to get a lot of touches in general has value, and you can see it up there as T%, that’s the total amount of touches these teams are getting. E% is the % of block touches that are block errors. (To be honest, it’s a bit confusing that VM lists them like that, on slightly different scales). And then you can see B:E ratio up there, which is a combination of the ability to block balls and not get tooled.
In Part 1, I mentioned there would be three broad profiles for blocking:
Teams that are relatively better at stuffing the ball.
Teams that make relatively few blocking errors
Teams that get a relatively high amount of touches, of any quality.
The 10 teams listed above are the Stuffmasters. These teams are relatively better at stuffing the ball, compared to the other aspects of blocking. Generally speaking, stuffing balls is good, avoiding getting tooled is good, and getting touches is good. But when I do these profiles, I am looking at teams that are relatively better in one area or the other. Meaning: it’s not just a list of the top-10 teams at Stuff Block %, it’s the teams that rank the highest in Stuff % compared to where they rank in the other 2 categories.
Winning And Losing
I was surprised at how low the correlation was between Stuff % and Winning and Losing. In this sample, the correlation between Stuff% and Win% was 0.16, which is not nothing, but it’s not super-high. For example, the win correlation for Ace:Error Ratio was 0.33.
Let’s look at the other correlations I pulled that specifically relate to blocking:
0.16 - Stuff %
0.07 - Touch %
0.38 - Error %1
0.35 - Block:Error Ratio
Stuff % is non-zero, but it’s lower than both Block:Error Ratio or even simple Error %. Interesting and counterintuitive! But… there is a little more context here because this is a self-selected sample.
Have any of you read the book Grit? It’s a good book and a worthwhile read. But, there’s a bit of a fatal flaw if you take a reductionist reading. You could read this book and say, “hey, when they measured things related to physical ability or intelligence, they didn’t correlate to success at a military academy, the only thing that correlated was grit, so physical ability or intelligence and grit is all that matter so just train your kids to be gritty and that’s all you need.”
But you’d be ignoring that the population of cadets at a military academy is already selected for physical ability and intelligence. Basically every cadet was 3.0+ in HS and most of them were 4.0. Close to all of them were varsity athletes and most of them were team captains, all-league performers, etc. So because they are already screened for those qualities, then grit does become the differentiating factor. But it’s a good bet that if you just took 100 truly random 18 year-olds, a significant portion of them would be unable to handle the rigorous curriculum demands at West Point and then high school GPA or SAT or whatever would start to correlate way more with success.
Likewise, 40-yard dash time isn’t highly-correlated to success as an NFL wide receiver.2 But this is because all of them are fast! Not only is it near-impossible for a guy who runs a 5.0 40 to be a good NFL receiver, he won’t even get the chance, because he wouldn’t even be starting in college. Compared the population sample of successful college receivers, 40-yard doesn’t appear to be a defining factor for NFL success. But compared to a broader population sample (like “all football players”), 40-yard is really important.
What Does That Have To Do With Blocking?
I’d hesitate to look at those broad correlations above and say, “hey, Block Error % has a higher correlation than Stuff Block % and basically the same correlation as B:E Ratio, all that matters is that you don’t get tooled. So don’t care about the physicality of your blockers and just get way out on the line to make sure you don’t get tooled.”
NCAA Top-100 programs are filled with players who are pre-selected to be physical blockers. What this information does tell us is, given that you have physical blockers, your ability to not get tooled is just as impactful (maybe more) than your ability to stuff the ball. “Everybody in the Big 10 touches 10’ but the good blocking teams stay square and know when to pull down and don’t let their opponents rack up chintzy tools.” There’s also probably some additional info about defensive quality too, because one team’s block tool is another team’s rundown dig.
Stuff % v BPG
I’m ranking these teams by their Stuff Block %, as a rate of total opponent attacks, rather than Blocks Per Game, which is the more common stat. I don’t think there’s too much wrong with BPG. This is a little different than say, Digs Per Game, which I think Dig % is vastly superior to. First of all, Stuff % doesn’t quick pass the human-scale test as well as Dig %. It’s a little easier for players to parse 60% v 70% than 7% v 8%, for some reason. It just seems to register a little more. Also, BPG tends to correlate a little better with actual blocking ability than DPG. So feel free to use BPG, I wont’ shame you for it. But, in theory, Stuff % is a little more accurate.
Even Better: B:E Ratio
There’s also B:E ratio. I didn’t use B:E ratio to form any of the Blocking Profiles, because it’s a combination stat that combines both blocking for stuffs and avoiding errors, and I wanted to look at profiles where those two were untangled from each other. But B:E ratio is one of the main stats I like to use when working with teams and giving them info on blocking.
Why?
First of all, it has a higher correlation to winning than stuff blocks, stuff %, etc.3
Second of all, it’s a nicely human-scale number. 1.0 (1:1) is a great target for the best individual blockers. 0.75 (3 blocks for every 4 tools) is a great target for the best blocking teams, as we see above. 0.67 (2 for every 3) is pretty good. 0.5 (1-to-2) might be okay for a smaller setter but it’s getting a little sketchy for a team. You can kind of translate them into hitting efficiencies, right? 0.67 block ratio means the other team essentially hit 0.2004 when they hit into or off the block. 0.5 B:E ratio means the other team hit 0.3335 when they hit into or off the block.
Finally, good blocking teams do block more balls on a pure BPG rate. And teams that block more balls overall do win more than teams that don’t, on average. However, especially in an individual match, your BPG is a measure not just of how good you were at blocking, but also how aggressive the other team was about attacking your block. For a while, I used the 2018 BYU - Texas Regional Final (aka “Elite 8”) match as an example in some of my seminars. First of all, because it was a great match between 2 really good teams. And second, because a big part of the narrative after that match was, “BYU won despite being outblocked 15-8 by Texas.”
However, when you add in some more context:
BYU Block/Defense
8 Stuff Blocks
9 Times Tooled/Errors
37 Digs on 60 Chances
Texas Block/Defense
15 Stuff Blocks
16 Times Tooled/Errors
26 Digs on 48 Chances
From that lens, I’d argue the BYU and Texas blocks were similarly effective, but the BYU hitters went after the Texas block a lot more. And the flip side, it wasn’t as reported, that the BYU floor defense was so much better either. Yes, they had 11 more total digs, but they also had 12 more chances to dig. On a % basis, they were only a little better.
When you add in context to the block/defense numbers, it was actually less of a story of “Texas was better blocking and BYU was better defending” it was more, “BYU hitters choose to attack the block more, Texas hitters choose to avoid the block a little more, and on this night, it worked out a little better for BYU.”
Again, this is the goal of using analytics, to help us see the reality of the game a little more clearly, so that we can respond better from a training or strategy perspective.
Technically it’s a negative correlation, because fewer errors = more wins, but I think it’s just easier on the eyes to absorb them as positive correlations.
Weirdly, it is more correlated for running backs, particularly when combined with weight. For an NFL running back, apparently you’re just trying to maximize your momentum in a very pure mass*velocity sense.
Surprisingly, in the above correlations, the correlation between just E% and winning is just as high as B:E ratio. I suspect it’s because VM doesn’t assign errors, as I do, to block that gets “bounced on”, ie, allows an almost-undiggable ball. For example, if I have a small middle blocker and the other team sets a quick and my small middle is late jumping, such that she barely has her hands above the net, and the other team detonates the quick. I generally assign that as a block error and not a digging error. VM will give it as nothing if the ball is bounced and a dig error if a defender managed to get some kind of touch on the ball. When you do this grading system, small blockers who don’t get tooled much but get bounced-on sometimes tend to be more accurately assessed than the way VM does it, and you’ll see the B:E ratios be a little more correlated than just E.
(3 - 2) / 5 = 0.2
(2 - 1) / 3 = 0.333