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The insight is provided by Steve Gera, a former US Marine, NFL coach and Co-Founder of Gains Group, in his role as moderator of this Keiser Webinar titled Data Decision Making in a High Performance Environment. He continues: “[Kelly said:] ‘That information is completely true but I cannot do anything with it.’”
While coaching reactions such as Kelly’s are common across sport, they are not inevitable. Joining Gera to explore the question of data’s role in the decision-making process were Kate Weiss, the Director of Sports Science at the Seattle Mariners of MLB, and Jordan Ott, Assistant Coach with the NBA’s Brooklyn Nets.
Weiss, who joined Seattle from the LA Dodgers last year, explained that datasets are increasingly prominent in the performance questions the club is seeking to answer. Ott, who has been in New York for six seasons, is part of a team that uses data to inform its decision-making as the franchise continues to evolve.
Here are the ten key considerations to emerge from the discussion.
1. Data can inform your athlete development plans
Weiss explains that Mariners draftees are provided with a personalised, holistic player development plan that is shaped by each practitioner and hones in on specific aspects of their development and how progress will be tracked. “That’s why that initial planning phase, that goal-setting phase, is really critical for us because that’s what sets up the tracking plan. ‘OK, if these are our goals, we’re going to measure this, this and this,” says Weiss. “Knowing what we’re trying to develop makes it a lot simpler to come up with a plan for what to measure and the frequency of the testing.”
2. Relationships are a source of ‘soft’ data
“That’s where it starts and ends,” says Ott of those conversations he and his colleagues have with Brooklyn’s players. “The coaching side to it is that you do have to develop a relationship with the player.” It resembles a partnership and, as Gera points out, it is important to find out information that the numbers cannot tell you or to ask athletes what they feel are their strengths and weaknesses. Ott adds: “We’re trying to maximise each player’s strength so it’s less about what we want as a coaching staff, it’s more about maximising the strengths of the personnel that we have and it’s about us being flexible.”
3. Disseminating data insights
In addition to clubhouse meetings, the Mariners, as Weiss explains, ensure that their data insights are delivered in both written and visual form. “We actually have a team of people to help develop the reports in-house,” she says. “Ahead of new report development, we’re all meeting together as staff to discuss the pieces of information that are most helpful in that day to day decision making, as well as on longer time horizons. We do a very good job of tailoring it to the key stakeholders, including players, making sure the information is clear, straightforward and easy to understand and addressing questions it leads us as a group to come up with.”
4. Using data to inform testing protocols
Gera says that the ‘Holy Grail’ is taking data and linking it to specific tests that track athletic development. Weiss walks through a hypothetical process with a Mariners pitcher. “When they come in, we’re going to test range of motion, we’re going to test movement capacity; how are they going to move in a general sense and a baseball-specific sense,” she says. “We’re going to look at the different components of strength, speed, power, we’re going to look at body composition. All these different things that we know contribute to and help support what they do on the field. Then what we’re going to do is look at the on-field data and link that back and go ‘OK, maybe there’s issues with their shoulder separation on the mound.’ We’re going to look through everything and go ‘OK, is it coming from a range of motion issue? Is it coming from just a movement capacity issue?’ Or if it’s not those things maybe it’s just a coaching issue that we have to work on and come up with specific drills.”
5. Balancing long-term and short-term goals through data
There is a need in sport to balance game to game player development with the need to develop a player to a certain point in the future. The balance between tactics and strategy, as Gera puts it. Ott says that while it is hard to measure development game to game, long-term athletic development can have a knock-on effect on skill development. “I have one guy who’s a really good shooter, so obviously the shooting piece is important, but what he really improved was his ability to get directly to the basket. He’s not just a shooter, he’s a driver and a shooter and it’s opened up the avenue to him having a longer career, in my opinion.”
6. Data-informed markers for scouts
The Mariners are better-placed than ever to provide detailed briefs to scouts. “With HP data, we can provide more colour and that helps you to discern between two players that may play very similarly on field. With this data, you can make a more informed decision about who is the better option,” says Weiss. “For us, understanding what types of players we’re looking for, the specific qualities we’re looking for on the field, and then providing education from that general athleticism standpoint or an injury standpoint.” The benefit is clear: “You can start to put together recommendations that help the scouting process from the other side as well.”
7. Using data to assess risk factors
Whether it is a draft pick or a free agent, data can inform the levels of risk a team is willing to take when building their roster. “In the first three or four years we were taking young guys that maybe physically had to grow,” says Ott, referring to the Nets’ initial rebuild under General Manager Sean Marks. “We’d take a younger guy that physically wasn’t ready but had good character and knew that would work. We’d bet on that person.” Having signed a series of high calibre free agents, there has been a shift in the team’s thinking. “A big key to where we’re willing to bet now are guys that have defined skillsets that would fit with our superstars and how to make our elite players more elite, and how we fit those guys in that can help those three guys is the challenge we took on last summer.”
8. Data as a driving force of research & development
“In game,” says Weiss, “we know that if we adjust launch angle that can improve the hitter’s ability to hit it out of the park. We can think that if we want to focus on launch angle and we’ve seen these trends, how are we optimising drill selection? What are we doing from a strength & conditioning standpoint? Are there ties to other components as well? And by exploring those things it helps us to come up with a new way of training and developing and creating drill sets for players. Or maybe it’s the implement itself. Going through, looking at what’s changing and how that’s making a big difference and what’s tied to that is how we’re thinking long term.”
9. Innovation can emerge from a single dataset
As Joe Shulberg, a coach at English Premier League club Norwich City points out in a question submitted to Weiss and Ott, we may think that innovation comes out of the blue, but it’s often a datapoint that influenced an idea. Ott concurs, adding: “The thing about basketball is that it’s so interconnected, so what we’re doing offensively affects us defensively. Maybe something we’re doing offensively is hurting us defensively and if the net benefit is not good enough then we have to learn to adjust how we’re coaching the team.” Ott highlights the reluctance of officials to give free throws in the current NBA and Gera ponders a hypothetical future where the GMs of the league request bigger, stronger and faster players as a consequence.
10. Data has its limits
Ott and Weiss touch upon the general limitations in data collection in the major leagues, but current datasets have very little to tell us about mindset, game intelligence, self-sufficiency, motivation or leadership potential. “There’s no magical test out there, no magical number, no psycho-graphic test,” says Gera. “The teams and organisations I’ve seen do it the absolute best typically mimic what the FBI, Special Operations and those folks do. They have intelligence units that go out and gather massive amounts of soft data and information and process that into a decision-making matrix that helps them find if that person is a red-line risk or whether or not a person has some of the soft traits you can actually mould.”
Kate Weiss of the Seattle Mariners and Jordan Ott of the Brooklyn Nets discuss their teams’ complex relationships with datasets and how they inform their work on a collective and individual level.
A Webinar brought to you by our Main Partners
The insight is provided by Steve Gera, a former US Marine, NFL coach and Co-Founder of Gains Group, in his role as moderator of this Keiser Webinar. “[Kelly said:] ‘That information is completely true but I cannot do anything with it.’”
While coaching reactions such as Kelly’s are common across sport, they are not inevitable. Joining Gera to explore the question of data’s role in the decision-making process were Kate Weiss, the Director of Sports Science at the Seattle Mariners of MLB, and Jordan Ott, Assistant Coach with the NBA’s Brooklyn Nets.
Weiss, who joined Seattle from the LA Dodgers last year, explains that datasets are increasingly prominent in the performance questions the club is seeking to answer. Ott, who has been in New York for six seasons, is part of a team that uses data to inform its decision-making as the franchise continues to evolve.
