Welcome to NL Ice Data!
To complement the current Swiss hockey statistics resources, I decided to launch a new Swiss hockey website entirely focusing on hockey statistics and called NL Ice Data. The goal is to present statistics for the the National League, Swiss League, Junioren Elite A and Novizen Elite. By using most of the different statistics the SIHF displays on its website this platform aims at providing new interactive tools to improve the evaluation of teams, players, goalies and coaches' performances and to better understand the game of hockey:
New tools, projects and statistics along with some projects will be added to the platform. News and website's
updates will be shared on the following Twitter accounts:
@NLIceData
|
@spz19
If the League publishes its proprietary data, in whatever format (.json, .xml or else), this website will be able to add some more
*advanced*
statistics for individual players.
This website is the result of a few hundred hours of work as part of a project made under the supervision of Hockey-Graphs through their Mentorship program. Hockey-Graphs is one of the most renowned, and deservedly so, analytical hockey resources in North America and regularly publishing research about various hockey topics (contracts, tactics and much more).
If you plan to use the different charts, table or data coming from this website, please link back to the website or Twitter accounts.
There is a possibility that some developments/data/charts contents will be made available through subscriptions in the future. The subscriptions will be affordable and will be reinvested in the website.
For any commercial use of the available content on this website, please send me an e-mail first at: nl.ice.data@gmail.com
Do not hesitate to reach out would you have any remarks/suggestions or spot any error:
For other hockey statistics on Swiss hockey, you surely know the following classic resources:
For some more
*advanced*
hockey statistics on Swiss hockey, you can also use the following resources:
Thank you for your visit!
Players - Statistics
Players - Statistics - Game Logs
Goaltenders - Statistics
Goaltenders - Statistics - Game Logs
Teams - Statistics
Players - Shot Tracker - Maps
Players - Shot Tracker - Statistics
Goaltenders - Shot Tracker - Maps
Explanations
These filters were added to better break down goalies' performances.
Goaltenders - Shot Tracker - Statistics
Explanations
These filters were added to better break down goalies' performances.
Teams - Shot Tracker - Maps
Explanations and use
Filters - Explanations
These filters were added to better break down teams' performances in different situations.
Teams - Shot Tracker - Statistics
Filters - Explanations
These filters were added to better break down teams' performances in different situations.
Score & Venue Adjustments
Score adjustmentThe game is played differently whether a team is leading, tied or trailing. Leading teams will, on average, play sound and safe hockey, with a more defensive mindset, while trailing teams will, on average, be more aggressive and offensive. As such, trailing teams are expected to generate more shots than leading teams. This is called score effects and it needs to be taken into account. A team should not be overly penalized for trying to protect a lead.
As such, shots taken by trailing teams have a lower weight than shots taken by leading teams. After applying these adjustments, numbers are more comparable between teams and better reflect their performances. It was also shown that Score & Venue adjusted statistics were the most predictive of goals and wins.
Venue adjustmentHome teams are, on average, expected to generate more shots than visiting teams. As such, adjusting for this effect will reduce the weight of shots taken by home teams, while increasing the weight of shots taken by visiting teams.
LitteratureYou can also learn more about the score-adjustment methodology here or here in these pieces from Micah Blake McCurdy. In his pieces, Micah computes his adjustment coefficients by in-game situations (5v5). For the National League, the coefficients were computed using all situations, as the overall sample size of the data is lesser.
Expected (xG) Goals Model
If the Corsi, Fenwick or shots statistics are proxies for puck possession, an expected goals model will inform about the quality of the chances or the probability that shots will end up as goals. For example, A shot from the blue line will not have the same chances to end up as a goal as a rush chance.
The current model is a slight variation of the model that was previously used for the Shot Quality charts for players. It includes the following variables: distance, angle, rebound, rush, penalty shot, offwing, as well as the strength state. Every shot attempt directed at an empty net have a zero xG value.
Except for two minor changes made to the model, this is the same as the one used by Daniel Weinberger for his National League predictions. Thanks to him for sharing it.
Data quality
It's still a topic, but it deserves its own and complete article. Note that at the current state of things, HCD numbers, and more specifically its xG numbers, may be underrated. The same can be said for HCD players' indiviual performances.
There can be some differences between data in this tab and the tab with teams' overall statistics, especially for the different Corsi statistics. The reasons will be further explained in the data quality article. Shortly, some teams overcount blocked shots, meanwhile some others (like GSHC, HCL or SCLT) undercounts them.