What are age curves? When do forwards and defense reach their prime? Which players have defied expectations? Find out the answers to these questions (and many more) on this episode of Ice Analytics. I will be joined on the Stat Chat by Matthew Klink, co-host of The Discussion Five Podcast to talk about the Detroit Red Wings.
Why were so many coaches fired this year compared to previous seasons? How did teams perform after they replaced their coach? Find out the answers to these questions on this episode of Ice Analytics. I will be joined on the Stat Chat by Isha Jahromi, co-founder of The Hockey Podcast Network and host of The Sota Pod.
I debuted the Annual Rate Charts (ARCs) on twitter over a month ago and since I’m unleashing the Tableau to the public, I wanted to ensure proper documentation was available.
You can find the link to the interactive Tableau Charts HERE!
What are Annual Rates Charts?
Annual Rate Charts (ARCs) are a way to measure the production and expected production of a player relative to their time on the ice. Production is quantified using the Goals Above Replacement (GAR) and Expected Goals Above Replacement (xGAR) models from Evolving-Hockey. If you want an in-depth analysis of how GAR is calculated, documentation is available here. In short, GAR is a single number that captures the contribution of that player in different game situations. GAR is subdivided into several categories, including even strength offense & defense, power play, penalty kill, takeaways and faceoffs. The number for each player represents the number of goals more (positive) or less (negative) that a player contributions relative to a replacement-level player (by position).
Expected GAR (xGAR) is also a single number assigned to each player, which is calculated based on the on-ice performance of a player (including rates, quality, shooting and goaltending). This number is the expected number of goals more or less above replacement-level that the player should contribute, based on their on-ice actions. In other words, xGAR represents the performance of a player, while GAR represents the results of that performance. When I tested the relationship between these values, I find that xGAR captures approximately 86% of the variation in GAR, which is quite substantial for a model including human subjects.
Annual Rate Charts (ARCs) can be differentiated from many other publicly available visualizations using GAR and xGAR data as it is relative to the amount of time a player is on the ice. These rates can be differentiated from many other popular visualizations that display GAR data in aggregate form. There are strengths and weaknesses to both rate and aggregate data, but both are useful in their own way.
If you haven’t heard, the NFL dropped its schedule on Thursday, May 7th. Using the fantastic site playoff predictors, you can predict the outcome of EVERY. SINGLE. GAME. That’s exactly what I did. I won’t display the weekly results, but I will provide my predicted record for each team, based on nothing more than my gut.
|#||AFC North||2020-21 Record||2019-20 Record|
The Ravens are bound to regress from last years’ historic season. The retirement of perennial pro-bowl guard Yanda will reverberate throughout the rushing and passing game. It won’t help that the rest of the division should improve as well. Every team in the AFCN seemingly improved this offseason, so the Ravens might not get as many “free wins” in the division this year.
Which teams have benefited from having the most “luck” over the past decade? Which teams have had bad “luck”? Find out the answers to these questions on this episode of Ice Analytics. I will be joined by Andy Hammond from The Broadway Boys Hockey Podcast to get his thoughts on the Rangers’ bad luck over the years.
Which teams are top-heavy and rely on a few forwards to generate offense? Which teams have more balanced scoring throughout their lineup? Are balanced teams more successful than top-heavy teams? Find out the answers to these questions on this episode of Ice Analytics. I will be joined by Jon from The Oil Country Podcast to get his thoughts on the Oilers, the most top-heavy team in consecutive seasons.
What is the relationship between hitting and penalties? Do players and teams that hit more often receive more penalties? Find out the answers to these questions on this episode of Ice Analytics. I will be joined by TJ and The Grumpy Old Man from the Never Say Die Podcast to talk about the Islanders, the most disciplined and physical team in the league.
IT’S FINALLY DRAFT WEEK!!! (UPDATED ON 4/23)
This is the one and only mock draft that I will publish, although I reserve the right to alter it prior to 8pm on Thursday. This is a combination of what teams SHOULD do and what they WILL do based on the evidence we have.
1. Cincinnati Bengals – Joe Burrow (QB, LSU)
Mark this pick in sharpie, there’s no way the Bengals trade away a bonafide franchise quarterback, even for multiple 1st rounders. Burrow was the highest graded QB by PFF in the past 6 years. He finished last season with 5,671 yards, 76.3 completion percentage, 60 touchdowns and 6 interceptions en route to a national championship. Case Closed.
2. Washington Redskins – Chase Young (EDGE, OSU)
The Skins have a decision to make: do they move back and collect a kings random of picks or select the best EDGE since Myles Garrett? I suspect they will opt for the latter and upgrade their pass rush by selecting a generational player in Chase Young. He led the nation last year with 16.5 sacks and 6 forced fumbles while also amassing 21 tackles for loss in only 12 starts.
3. [DET TRADE] Miami Dolphins – Tua Tagovailoa (QB, Bama)
Book it: someone will trade with the Lions to ensure they get the second best quarterback in the draft class – Tua Tagovailoa. The Lions could use the extra draft capital and I suspect the Dolphins and Chargers will both be in the running. Everybody knows that the Dolphins are going to draft a QB this year, the only question is “which one?” Tua started 24 games over the course of his sophomore and junior seasons, finishing with 7451 yards and nearly an 8:1 TD-to-INT ratio. If not for a season ending injury, there might be more of a controversy at #1. But due to his injury and some concern about his long-term durability, Tua will be the second QB taken.
What is the loser point and what are its alternatives? How would a winner-take-all or tiered point system change the playoff picture? Find out the answers to these questions on this episode of Ice Analytics. I will be joined by Shane Ryan from the SENSturion Overtaking Podcast to chat about the Senators and ways to improve the NHL.
How does the back-to-back impact team performance? Which teams have performed the best with no rest? Find out the answers to these questions on this episode of Ice Analytics. I will be joined by Michael Farley from the Clean Skate Pod to chat about the Dallas Stars, a team that has performed poorly in back-to-back games over the past decade.