Inside the Sabres’ pre-draft analysis process

The draft preparation process

The analytics team begins its process by collecting data on each draft-year eligible prospect across a variety of parameters and evaluation metrics.

With this data, they can narrow down the number of players Sabers scouts can focus on.

“If you look at the draft-eligible players for this season, there are about 32,000 players in our data set,” Barlowe said. “If you include the plus-1 and plus-2 players from the draft year who were eligible in previous drafts, that hits around 100,000 players – numbers that are just very difficult to track all at once. Much of our process helps – especially later round picks – to reduce those numbers into manageable numbers that our scouts can then monitor.

While scouts go to observe players, Ventura’s team monitors the high-quality data they have on each prospect. The challenge is to decide how to appropriately weigh all the information received from each assessment context.

NHL teams are looking for draft-eligible prospects from over 30 different leagues around the world. In Europe, in particular, various factors impact the rating over the course of a single season, including age of competition, level of competition, and tournament flow.

“For some players, we have over 10 different evaluation metrics that we analyze over the two seasons before they’re drafted, just between the different leagues and tournaments they play in at different ages,” Ventura said.

The main objective is to identify players who perform consistently across their different evaluation contexts.

“It really starts with understanding who the players are as they come into the season and gradually updating our opinions on those players as we get more and more data coming into our systems throughout of the season,” Ventura said.

Ventura’s group has developed models that project future player performance, placing more emphasis on certain areas of the game. The process helps Ventura, Galamini Jr. and Barlowe track metrics that players can control, such as effort, to select players who exemplify characteristics correlated with success in the NHL.

“That extra data kind of helps us identify players who score a lot of goals, but also do all those extra things that translate well to NHL success, players you would want on your team in the playoffs.” , Barlowe said.

Benson, for example, met many of the team’s criteria even though he was an undersized forward in the draft.

“Not only did he bring offensive production both as a scorer and as a playmaker, but Zach had a knack for contributing in a number of areas,” Galamini Jr. said. “For example, winning puck battles, puck handling and playing a good defensive game. He did things that are desirable traits at the NHL level and checked a lot of boxes for us.

“But obviously it gets risky when you like certain aspects of a player and you don’t like others. And that kind of thing happens as you go through a draft, you have to live with certain flaws and trying to understand how everything balances out in the end.

Ventura and his team then independently evaluate each prospect from a strictly statistical standpoint, using the agreed-upon process and criteria to form their own lists before the larger group comes together. The main challenge is preventing internal biases from coming into play and remaining objective in using data to tell the story.

The group agrees not to read any scouting reports or watch video on a player until they form a strong opinion based on the data. Often the analytics team’s opinion of a player will resemble the scouts’ opinion.

“It’s rare that our description of a player differs significantly from a scout’s description of a player,” Ventura said. “And so, I find that our opinion and that of the scouts have become quite similar after we developed the models that we use now for player evaluation and we really developed the depth with which we can look at the details of ‘a player’s game.