Of Passer Ratings and Power Plays: The Sports Data Viz Digest No. 1
By: Nihar Ullal
Tags: NHL, Sportsviz, Data Vizualisation, Sports, NBA

Next up, designer Paul Buffa has created two different graphs to display his change in heart rate on a run at specific locations. The side-by-side illustration of a scatterplot and heat map shows the viewer exactly where and when the heart rate changed based on elevation and accumulated distance. Paul’s pairwise matrix uses density contours to highlight trends based on the different variables. The combination of the two visualizations speaks volumes and tells a more complete story.

The additional context a heat map adds to the pairwise matrix uncovers deeper “relationships between elevation and heart rate.” I myself am not much of a runner, but for those out there, heart rate is a tricky vital sign to maintain. If you run similar routes or paths, this visualization would give you incredible insight: where your heart rate peaks and falls at what exact points on your run. It allows runners to better control their breathing and pace, based on the estimated heart rate.

Buffa provides an interactive version here: https://observablehq.com/@pstuffa/density-contour-matrix-for-geospatial-datasets.

Mesut Ozil’s Change in Effectiveness Over the Last Two Seasons — Tom Worville

Staying with the theme of tracking an athlete’s activity, designer Tom Worville has created a couple of soccer visualizations, specifically in this instance for Mesut Ozil of Arsenal (part of the English Premier League). Whereas Paul Buffa was previously helping track a runner’s route, Worville here is tracking the specific parts of the court, or field in this case, a player is most effective in!

The first graphic above denotes the change in Ozil’s total touches, stratified by area of the field. It is certainly interesting to note that Ozil’s activity declined by about 13 touches per 90 between seasons in the middle of the attacking third, ostensibly an area of the field that he should be dominating. This change is highlighted in even greater detail in the passing maps below:

Mesut Ozil’s passing in 2017-18

The first map above shows the start points for Ozil’s most dangerous passes during his 2017-18 season. Compared to the second graphic below, which shows his most dangerous passes in the following season, the viewer can see the passes originated more from the sides, and deeper into the field, as compared to more directly through the middle in 2017-18. These graphics articulate the clear dip in effectiveness for Ozil’s passes and touches with minimal distractions.

Mesut Ozil’s passing in 2018/19

Calgary Flames’ Shots and Goals — Bill Tran

We have a hockey visualization! Of all the sports out there, hockey intrigues me personally because I possess very little knowledge on the mechanics of the game. It is such a physically intense sport and so incredibly fun to watch live. Unfortunately, I’m from Atlanta where our hockey team left us almost a decade ago. That won’t stop me from taking in Bill Tran’s visualization on the Calgary Flames’ shooting last season, however.

The Flames scored an incredible 289 goals last year, second-most goals in all of the NHL. Tran broke down the goals by each specific player and distance. Unsurprisingly, he showed that the players at the top of the chart (who scored the most) tended to score most frequently from shorter distances. Still, players like Elias Lindholm, however, had a more varied distribution. But that’s just the forwards. How’d the defensemen do when called upon to contribute to the scoring fun?

The six Calgary defensive players scored most of their goals from beyond 25 feet or more! The shot attempts are skewed more to the right between the 35 to 70 foot range. In all likelihood, this is because defensive players tend to stay further back from the goal in general and as such are taking more speculative attempts.

This data could be of the utmost importance to not only the Calgary team but their opponents as well. Let us imagine both the Calgary team and opposing hockey teams have this graphic on hand. For Calgary, they could start confusing opponents’ schemes by either having defenders start to move up and take closer shots as well as having offensive players shoot from farther away! Likewise, opponents could start planning defensive schemes to anticipate who might be taking the shots and when they might be occurring. The predictive nature of this graphic ties it in with the adjacent ideas presented previously.

Check it out here: