Rush Shots and Defensive Zone Play of Maple Leaf Defensemen

The other day over at PensionPlanPuppets.com there was a post by Draglikepull looking at zone exits by Maple Leaf defensemen for the first half of last season. If you haven’t seen it yet, definitely go read it. I wanted to compare the zone exit data to my rush shot data which I have calculated from play by play data as explained here. If we can find good correlations between zone entry/exit data and my rush shot data that would be an excellent finding because the zone entry/exit data need to be manually recorded and is very time consuming. Thankfully this

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Leafs, Kings and Devils – Rush goals and shooting percentage

Tyler Dellow has an interesting post on differences between the Kings and Leafs offensive production. He comes at the problem from a slightly different angle than I have explored in my rush shot series so definitely go give it a read. These two paragraphs discuss a theory of Dellow’s that is interesting. That’s the sort of thing that can affect a team’s shooting percentage. To take it to an extreme, teams shot 6.2% in the ten seconds after an OZ faceoff win this year; the league average shooting percentage at 5v5 is more like 8%. Of course, when you win

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Rush Shot Save Percentage

I have written a couple of posts (here and here) on rush shots as it relates to shooting percentages and I investigate this further at a later date. First though, I wanted to take a look at save percentages on rush and non-rush shots. Let’s start by looking at teach teams 5v5 road save percentages for the past 7 seasons combined. A few observations: Whoa Tampa! That’s a dreadful save percentage on the rush, 2.5% below anyone else. More on this later. The teams with the best save percentages on the rush are Anaheim, Phoenix, New Jersey, and Boston. r^2 between

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Rush Shots Leading vs Trailing and Home vs Road

Yesterday I introduced the concept of rush shots which are basically any shot we can identify as being a shot taken subsequent to a rush up the ice which can be determined by the location of previous face off, shot, hit, giveaway or takeaway events. If you haven’t read the post from yesterday go give it a read for a more formal definition of what a rush shot is. Today I am going to take a look at how rush shots vary when teams are leading vs trailing as well as investigate home/road differences as arena biases in hits, giveaways

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Introducing “Rush” shots

I have been pondering doing this for a while and over the past few days I finally got around to it. I have had a theory for a while that an average shot resulting from a rush up the ice is more difficult than a shot than the average shot that is generated by offensive zone play. It makes sense for numerous reasons: The rush may be an odd-man rush The rush comes with speed making it more difficult for defense/goalie to defend. Shots are probably take from closer in (aside from when a team wants to do a line

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WOWY Analysis of Umberger for Hartnell trade

More often than not the first thing I look at when I want to evaluate a player is their WOWY stats to see if the player boosts the performance of their teammates or suppress it when he is on the ice. Let’s take a look at a WOWY comparision of Umberger and Hartnell starting with some links to their WOWY pages. Hartnell 2013-14 (5v5) Hartnell 2012-13 (5v5) Hartnell 2011-12 (5v5) Umberger 2013-14 (5v5) Umberger 2012-13 (5v5) Umberger 2011-12 (5v5) When on any of those pages you can click “Visualize this table” to get some charts that I find are often a quick way

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Being honest about “possession” stats as a predictive tool

I often feel that I am the sole defender of goal based hockey analyitics in a world dominated by shot attempt (corsi) based analytics. In recent weeks I have often heard the pro-corsi crowd cite example after example of where corsi-based analytics “got it right” or “predicted something fairly well”. While it is always good to be able to cite examples where you got things right a fair an honest evaluation looks at the complete picture, not just the good outcomes. Otherwise it is analytics by anecdotes which is an oxymoron if there every was one. For example, Kent Wilson

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Are coaches any good at identifying defensive players?

A while back I came up with a stat which at the time I called LT Index which is essentially the percentage of a players teams ice time when leading that the player is on the ice for divided by the percentage of a players teams ice time when trailing that the player is on the ice for (in 5v5 situations and only in games in which the player played). LT Index standing for Leading-Trailing Index. I have decided to rename this statistic to Usage Ratio since it gives us an indication of whether players are used more in defensive

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Measuring persistence, randomness, and true talent

In Rob Vollman’s Hockey Abstract book he talks about the persistence and its importance when it comes to a particular statistics having value in hockey analytics. For something to qualify as the key to winning, two things are required: (1) a close statistical correlation with winning percentage and (2) statistical persistence from one season to another. More generally, persistence is a prerequisite for being able to call something a talent or a skill and how close it correlates with winning or some other positive outcome (such as scoring goals) tells us how much value that skill has. Let’s look at persistence first. The

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Estimating actual randomness in goal data

If you have been following the discussion between Eric T and I you will know that there has been a rigorous discussion/debate over where hockey analytics is at, where it is going, the benefits of applying “regression to the mean” to shooting percentages when evaluating players. For those who haven’t and want to read the whole debate you can start here, then read this, followed by this and then this. The original reason for my first post on the subject is that I rejected Eric T’s notion that we should “steer” people researching hockey analytics towards “modern hockey thought” in

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