The Calgary Flames scored a lot the past year. En route to clinching the 2019 Western Conference regular season, they scored 289 goals, which was good for a tie for second in the league with the San Jose Sharks. Only the Tampa Bay Lightning scored more with 325 goals.

To score as many goals as the Flames, you have to take many shots, and there is no denying that the Flames definitely did. The Flames took 3,688 shot attempts over their 82 games. Quite frankly, that’s a lot of data to process. To understand it in a meaningful way, I’m breaking it down simply by looking at shot distances from the net for every player.

Calgary Flames shots and goals

To visualise every single shot by the Flames, I used beeswarm plots to show the distance from where a player took a shot attempt to the net. I wanted to recreate something similar to the work of Prashanth Iyer (@iyer_prashanth) in visualising goaltender statistics using beeswarm plots. I was drawn into how he highlighted key points and used it to further drive home the points he was trying to make.

In using beeswarm plots, the shot distribution for each player should make itself apparent and their playing style in terms of where they like to shoot can be easily discerned. I further separated the groups into forwards and defencemen since intuitively there’d be a large discrepancy between those positions.

So let’s delve into how the season panned out for Flames skaters in terms of where they shot the puck. Using shot distance data from MoneyPuck, each player’s shot distance beeswarm is plotted with their goals highlighted.

Forwards

The Flames had 13 forwards log at least 30 games played. When it came to taking shots and scoring, some were much more effective than others.

Led by Johnny Gaudreau, the diminutive winger typically liked to shoot when he was about 35 feet away from the net or less, and that was also where he most often scored.

Sean Monahan, Matthew Tkachuk, and Sam Bennett more often than not drove towards the net before taking their shots; a large fraction of the goals from these players came from in close. For Bennett, every single goal he scored came from less than 25 feet away from the net.

Another top performer for the Flames, Elias Lindholm seemed to shoot from wherever he wanted to, and scored from wherever he pleased. In a similar manner, Mikael Backlund and Michael Frolik also shot and scored from all over the ice.

When looking at Mark Jankowski, he did have a group of goals from in close, but doesn’t seem to shoot that close to the net in general. A likely reason as to why these goals are clustered could be largely due to his usage on the penalty kill. A lot of Jankowski’s goals included breakaways or odd-man rushes when the Flames were down a skater.

Progressing down the list towards the players that scored less often, it’s seen that Derek Ryan, Garnet Hathaway, Andrew Mangiapane, and Austin Czarnik were all a part of the “shoot from wherever” group as well.

Lastly, arriving at James Neal, his abysmal season is highlighted yet again. Despite taking a large number of shots, his shooting percentage was non-existent and consequently his goal count just wasn’t there.

On a whole, Flames’ forwards tended to shoot and score from within 50 feet. That’s not really surprising, but it fascinating to see how skilled players tend to get in closer before taking their shots.

Defencemen

On the defensive side of things, the Flames had Norris Trophy winner Mark Giordano taking the reins. He led the defence corps in shots and goals. His scoring came from all over the ice and quite a few came from right in front of the net. His ability to engage in an offensive play is clearly highlighted.

The other three defencemen in the Flames’ top two pairings weren’t quite as successful, but they pitched in where they could. T.J. Brodie had a few goals where he got in close, but most of his goals actually came from afar.

Travis Hamonic and Noah Hanifin had really similar shot and goal distributions all things considered, but one major difference was that Hanifin seemed to refrain from taking really far shots if possible.

Newly promoted full-timer Rasmus Andersson had quite a few shot attempts himself, but tallied just two goals, both of which were when he was near the blueline. His lack of scoring wasn’t from a lack of trying though, as he shot tally was quite impressive given his time on ice.

Andersson’s Swedish counterpart Oliver Kylington managed to one-up him and score three goals, all in which were much closer to the net than Andersson’s. Kylington didn’t take as many shots as the other defencemen did, despite playing nearly half a season, but he definitely made his goals count.

Most shots from the defence came were taken by the blueline, as evidenced by the distributions. But Calgary’s defence is known for activating and pinching when they feel the risk and reward is worth it, and that point makes itself pretty clear as well.

Main takeaways

These plots definitely aid in determining where a player likes to shoot and score from. The differences between forwards and defence is rightfully shown, but more interestingly, the differences within these groups really highlight the playing styles of each player.

However, there are a few shortcomings in the plots as well. When discerning shots just by distance from the net, the angle isn’t taken into account. Further, the shots plotted are all-situations, in which a player’s shot distribution should actually be impacted by their usage.

Another little caveat is that penalty shots aren’t plotted. Gaudreau’s goal against the New Jersey Devils isn’t shown, but it was the only penalty shot goal the Flames scored all season long, despite having five chances.

All in all, the Flames enjoyed great success this past season, and a large part of that was due to their offence. They scored often and they took their shots. Whether or not their performance is repeatable is yet to be known, but one thing is certain: you miss 100% of the shots you don’t take, but score on a few of the ones you do.

Check out these plots for the rest of the NHL!

Pacific | Central | Metropolitan | Atlantic

Data courtesy of MoneyPuck. Charts made with R using “ggbeeswarm” and modified using Adobe Illustrator.