Using data to define good gap control for defencemen

In Analytics, Coaching Hockey, Premium Articles by Ryan Stimson1 Comment

Whenever we talk about transition defence and how teams defend zone entries, the conversation inevitably turns to gap control.

Most readers know what gap control is, but just so we’re all on the same page: as the offensive player races up ice in an attempt to gain the offensive blue line, the space between them and the nearest defender is generally referred to as the defender’s “gap” and how well the defender manages this space is often referred to as their “gap control.” A defender with a small amount of space  between themselves and the offensive player is said to have a tight gap. The prevailing wisdom suggests that the tighter the gap, the more pressure applied to the puck carrier, but the greater risk of allowing the offensive player to skate past the defender.

How can we measure that risk/reward scenario? It’s a question we haven’t been able to answer as we do not have player location data available. That’s where data companies like ICEBERG Sports Analytics come in.

This season, ICEBERG has graciously given me access to 75 games of data including their player and puck tracking technology in addition to event data we’re familiar with now (entries, exits, passes, shots, etc.). I’ll be researching the data and sharing findings here and elsewhere online. The findings shared here will be geared towards actionable insights for coaches and your teams. Today it’s gap control!





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About the Author

Ryan Stimson


Ryan has written extensively on hockey analytics since 2013. He has pioneered work in player evaluation and game strategy, leading the popular Passing Project for several seasons. Ryan has contributed on analytics and published new research at Hockey Graphs, but also has written on using data to better evaluate hockey tactics. He consulted for RIT Men’s Hockey Team from 2015 - 2018 and coached a 14U team as well. Ryan is a Certified Level III USA Hockey Coach. He has published a book you can buy on Amazon, Tape to Space: Redefining Modern Hockey Tactics, that draws on insights gained from data analysis to optimize how teams should play hockey. See All Posts By Ryan



  1. it will be interesting to see the histogram for all the data points on a single distance. With all the data points distribution, it is really hard to tell. Once you have the histogram for each distance, the mean and error can be calculated.

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