The relationship between Rush Goals Against Percentage and Goals Saved Above Expected was much much weaker.

I found a decent relationship between Rush Goals Percentage and Goals Scored Above Expected at the team level in this small sample - a 0.51 R and 0.26 R^2. then it probably is going to underrate those chances. It makes intuitive sense - if the model can’t identify an odd-man situation, a cross-crease onetimer, a breakaway, etc. My hypothesis going in was that a good chunk of the non-luck portion of outperforming expected goals at either end of the ice was generating off the rush. Something else I was curious about was the xGoal of these goals compared to non-rush goals. Let’s start out with the stat I was most focused on: which teams scored the largest share of their total 5v5 goals off of rush chances? What I was most interested to see was which teams scored most of their goals off the rush, and which teams gave up the most goals off the rush.

In a few days, I’ll follow up with player stats. In this piece, I’ll be focusing on team-level metrics. This gave me the ability to look not only at which teams scored but who allowed them, who was on the ice for both teams, and individual player and goalie stats. To make the most of this tracking, which would have been basically impossible (or at least have taken up many more hours of my life) without InStatHockey, I decided to link my team-level tracking to the NHL’s play-by-play goal data as assembled by. Non-rush goals typically came off the forecheck or the cycle. I defined a rush goal as being a goal where a controlled zone entry occurred and a goal was scored within five seconds without being given away to the opposing team. While public expected goals models have rush chances baked into them (those shots and goals are part of the data that goes into building them), because of data limitations they cannot identify whether a shot came off an odd-man rush or breakaway or what have you. My hope with this was to gain some understanding of how teams and players were generating and giving up goals, and possibly even learn why certain teams were significantly under- or over-performing their expected goals so far. Inspired by this, I decided to take on a relatively limited but hopefully revealing project: tracking every 5v5 goal of the 2020-21 season so far based on whether or not it came off the rush. Corey Sznajder’s Tableau and Patreon display mountains of information that we would otherwise be without, and they have been an invaluable resource for anybody trying to work out the “why” behind player results. While tremendous value can come from intelligent analysis of what is available to the public, private data is expensive for a reason, and I am always interested when we get limited glimpses of what’s behind the curtain.Īs anyone who’s read a good amount of my work knows, certain members of the public have heroically filled that gap a bit by manually tracking NHL games themselves and making the data available to fans. Monarchs were sold to the organization.At this point, most analytically-minded hockey fans recognize that there is a distinction between the NHL data that is collected for public consumption (known as “play-by-play data”) and the far more extensive information collected by private companies like SportLogiq and Stathletes as well as NHL teams themselves. The elite level team was established in 2015 when the franchise rights to the New Hampshire Jr. The teams in the organization play their home games at the Extreme Ice Center. The organization is based out of Indian Trail, North Carolina in suburban Charlotte, North Carolina. The Charlotte Rush are a Tier III junior hockey team. USPHL Elite Division, United States Premier 3 Hockey League

Hockey rush team