Sports teams and organisations have spent the best part of a decade collecting data on athlete performance, but what does the next decade hold? Zone7 believe that the answer lies in making sense of it all. In fact, they refer to this as sport’s next ‘arm’s race’. This is just one of the themes touched upon in this Special Report, which is brought to you by our Partners at Zone7.
Complete this form to access your free copy of In The Zone, which delves into the growing sophistication behind data interpretation, the importance of openness and collaboration between stakeholders, particularly when addressing any reservations, and how data can transform the way business is done in the front offices of elite sport.
The Irish startup PlayerStatData says that their app helps to provide a holistic picture of young player development in soccer.
During the pandemic pause on sports, however, Brett recognized an even greater deficiency in the player development infrastructure, so he pivoted his startup, PlayerStatData, to address the Under-13 through Under-19 population. The target user is currently academy directors and player development coordinators, but Brett says further iterations will likely suit coaches and the athletes themselves.
The PlayerStatData app, which launched in the US and Canada earlier this month, seeks to provide objective performance data culled from video analysis, physical test results, a centralized library of coaching assessments and, crucially, a monitoring system for psychological and socio-economic insights.
“We want to see be a solution for all and to be an all-encompassing solution as well, which means that we want to be accessible, affordable and available to all clubs at all youth levels across the US and Canada,” says Brett, the CEO and co-founder of the Waterford, Ireland-based company. “And we want to give them the full picture of a player’s development.”
Context is critical. Family backgrounds and finances all play a role in player progression, especially in the North American pay-to-play model with costly club and travel teams representing an important pathway. Teenagers’ mental health and perspectives need to be considered, too.
“Coaches have become a lot more open to psychological output because, especially with the age that we’re looking, 13 to 19, there’s a lot going on physically and mentally with them at that age,” Brett says. “There’s a lot of stuff to understand with them too. So that’s where we want to get the best advice, because it’s important to get that right.”
For that, Brett has turned to Laura Finnegan, a lecturer in sport management at South East Technological University in Waterford, as an advisor. Her master’s thesis was in sports psychology, and her Ph.D. dissertation studied the organizational structure of talent development in Irish soccer. Finnegan has done research work on behalf of Uefa and US Soccer as well.
“It’s valuable everywhere to be able to see the player in the round,” she says. That 360-degree view, which PlayerStatData will incorporate piecemeal in future updates, is a novel approach to a market that does have several digital scouting video platforms, GPS wearables and new sensors already. “I really think that’s what’s going to set them apart,” Finnegan adds.
Malcolm Gladwell detailed in his book Outliers that a disproportionate number of NHL players were born in the early months—January, February and March—because the Canadian youth program cutoffs were at the start of the year, thus favoring the slightly older kids. Finnegan has noted similar patterns in academies in the United Kingdom and thus advocates for delayed selection of players because many physical skills don’t manifest until after puberty.
“It’s all stacked with boys that are our early maturers, and in the early years, all born earlier towards the cutoff as well,” she says of the academies. “That was one example of something that we could layer in so that you’re not just necessarily comparing Boy A with Boy B, but actually, you’re comparing boys with someone of the same maturity status as him. You’re trying to be fairer for those kids. For me, it’s just adding an extra lens for coaches.”
PlayerStatData has done some early work with the academy of Waterford FC, which competes in the League of Ireland’s First Division, and has attracted some early clients overseas such as Ottawa University Arizona, a nationally ranked NAIA program. PlayerStatData also sponsored a local Under-14 tournament where it did analysis for the participating teams, which included a team from the Blackburn Rovers, whose first team is one rung below the Premier League. Brett envisions a platform that’s truly customizable so that users can meet their needs no matter the staffing and resources.
“What’s useful is we did some bespoke design,” says Waterford FC academy director Mike Geoghegan. “So Colin sits down and asked me, what information am I looking for? What’s the sort of things that I want to track as a head coach? Because it may not be the same for every head of academy.”
For now, the PlayerStatData staff manually tags video and collects data, but computer vision algorithms developed in conjunction with professors at the local university are being developed. Brett wants that process automated within 18 months so that coaches only need to upload video into the app. “We want to get into a situation where it’s drag, drop, collect, and pick up the reports,” he says.
The Waterford academy, for instance, is staffed by part-time coaches who don’t always have the time to “extract and properly manage the data and draw insights from that data,” Geoghegan adds. “So I’m saying, I’ve got lots of recordings, lots of football, lots of coaches, but I’m not really getting this information in any way because it’s no one’s job.”
Brett sees the US and Canadian soccer systems as needing a tool like his to eliminate subjective coaching bias; the volume of players and vastness of geography make it hard for objective monitoring.
“It’s a bit of a wild west when it comes to pay-to-play and the sheer size of the market,” he says, adding: “There’s an openness to data, there’s an openness to finding that edge, it’s an openness to use a couple of innovations to get ahead, be that as a club or be as a player.”
This article was brought to you by SportTechie, a Leaders Group company. As a Leaders Performance Institute member, you are able to enjoy exclusive access to SportTechie content in the field of athletic performance.
This question was tackled by Gavin Benjafield of LAFC and Ben Mackenzie of Zone7 in our latest webinar.
Ben Mackenzie, a Data Research Analyst at Zone7, an injury risk forecast and load management platform, is talking at the organisation’s webinar titled ‘Blending Sports Science and Data Science’.
“Quite often, when people refer to injury prediction, I think the mind goes to ‘this injury, on this day, at this time, as a result of this action’ – and it really isn’t any of those.”
Instead, Zone7 can dip into its ‘data lake’ of over 200 million hours of performance metrics and over 10,000 injury instances to produce an injury forecast based on clean, consistent data.
Still, data science remains misunderstood across elite sport. “Sometimes data analysts and data scientists get blended together now that we have analytics departments,” said moderator Dr David T Martin, the Chief Scientist, Director of Performance at Performance Health Science. “Some people will say ‘I’m not a data scientist, I’m an analyst’.”
Mackenzie and Martin are joined in conversation by Gavin Benjafield, the Director of Performance at Los Angeles FC, who have worked with Zone7 for two years.
Mackenzie uses Benjafield and the club to further illustrate his point on injury forecasting. “We’re able to identify that ‘this’ player, if he continues on the path that he is, he might be outside a certain range, might be at risk or is a risk of an injury as a result of being outside of the norms for LAFC’s training data. Therein lies the risk forecasting. This player either needs to do more, we suggest that he does more, or we suggest that he does less to mitigate that risk of injury.”
Practitioners from across the globe logged on to listen to the trio discuss the distinction between sports science and data science, the misconceptions that abound, as well as the steps teams can take to better use the data they are collecting.
Here, the Leaders Performance Institute highlights the other key insights from the session.
Sports science vs data science
There is a perception in the sports science world that data science is just another element of the job. Benjafield shares the story of a job opening at LAFC. The position was for a data scientist and the job description made that clear, yet just 25% of the 200-plus applicants worked with data. “Sports science: your understanding of physiology, psychology, biomechanics, all those components are nothing to do with data science,” said Mackenzie. Whereas the data scientist’s ability includes “[collecting] data, clean data, and understand multiple programming languages as well as the ability to clearly express what your findings are – they are completely different disciplines.”
Should you outsource your data science?
To illustrate a point around using consultants, Benjafield spoke of his ability as a handyman around the house. He is adept at certain task but draws the line at electrics. At LAFC, Zone7’s forecasting services and AI fulfil the role of the electrician. “If you’re just going to absolutely outsource everything then you’re just going to be an organisation with a ton of consultants running around you. You’re actually not going to have any identity,” said Benjafield. “We’re not going to become a consultant circus, we’re going to strategically pick those that we believe are the electricians that we feel comfortable doing that by ourselves, but we still want to take ownership and do a lot of the things ourselves otherwise we become spectators in our own department and I don’t think anyone wants that.”
As Mackenzie said: “A sentence that is thrown around at Zone7 quite a lot: we are a weapon the practitioner’s armoury. You have the tool box, we are just the hammer. There are many other tools that can get jobs done or can be used for other jobs. It is up to the practitioner to use their skill, their interpersonal skill and their skills in other sports science disciplines, combining all those elements and information provided by Zone7 for them to come to an informed opinion and not data-led.”
Creating actionable steps
Actionable steps are essential when using data, as 100 metrics cannot be manipulated by someone in Benjafield’s shoes across 25 athletes. Minor adjustments and corrections are a good start. LAFC worked with Zone7, who retrospectively analysed a season of data, to hone in on five GPS-related metrics. “Three of those we were already monitoring closely, two of them were not, so I think that just helped us to get actionable items,” said Benjafield, who is mindful of the challenge of pleasing coaches who want as many players as possible available.
The future
Mackenzie and Benjafield wrapped things up by pondering where the future relationship between data science and sports science. “People are fearful of losing jobs or being overtaking by data or AI,” said Mackenzie of the sports world. “I think it requires a change of mindset, a change in appreciation of different skillsets, and an understanding that a different skillset offers different things. That’s where it needs to start. Mindset, openness and willingness.”
Relationships are important for Benjafield too. He said: “I’d like to still be in an industry where we are wearing fewer external devices but we are collecting more data and richer data; and that is translated. I don’t want to lose the relationship with the athlete.”
The former wide receiver discusses the use of data in the NFL and his work with Breakaway Data, the holistic data platform.
The Bills drafted him in the third round of the 2012 NFL Draft. Graham played two seasons in Buffalo, catching 54 passes for 683 yards and three TDs. He went on to play regular season games with the Jets and Saints while also logging time in training camp with the Titans, Eagles and Panthers and spending parts of three seasons with the CFL’s Montreal Alouettes.
Graham, now 32, completed his playing career in 2019 and turned his attention to coaching and data. He’s mentored numerous elite athletes in the Raleigh-Durham area while becoming a data advocate. For a spell, he also worked in Sportlogiq’s business development office on American football projects.
Graham has spent the past two years at Breakaway Data, a holistic data platform co-founded by the leaders of the Gains Group sports consultancy to monitor and improve their own fitness and performance. He is currently its head of performance and on-field application, but also just started a two-month stint with the Green Bay Packers as part of the NFL’s Bill Walsh Diversity Coaching Fellowship.
On when he realized the importance of data . . .
It started a little bit around the time I interacted with Philadelphia Eagles—that was in 2016—but the best and realest time I had a connection with data and analytics in football was definitely with the Panthers. And I’ll give you the story: we had always worn GPS devices, but we didn’t really get that stuff given back to us. It was just measured, tracked, probably used more against us than for us—but also to tailor our workouts to fit us, personalized.
Other than that, it wasn’t to the degree of… I call it ‘athletic expansion.’ Just for me in my athletic knowledge and IQ, there are things that I need to know, and I need real-time feedback to adjust. I wasn’t getting that until I was with the Panthers.
One day at practice, one of the strength coaches came by and said, ‘Dude, you ran 23 miles-an-hour on the GPS.’ I’m like, ‘What does that mean?’ He was like, ‘That’s the fastest we’ve ever seen recorded on the GPS units.’ I’m like, ‘OK, I really don’t know what that means.’ I mean, I’m a track guy. So I understand that we’re moving, but in my mind, I’m like, ‘When did I do it in practice? What did I do in preparation for that practice or even that data point to hit 23 miles-an-hour? What did I do to lead up to that? And then how do I replicate that? Or was it so high and so fast that I need time to recover?’
On the evolution of data in the sport . . .
It definitely has improved as coaches have gotten younger and the more that the data providers have created education around the space. It started with some [analysts] calculating some and then being like, ‘Hey, this is beneficial.’ And then a coach saying, ‘I don’t know what the heck this is,’ then a coach saying, ‘I kind of know what this is.’ I started between the two of them. Football has always been statistical and analytical. We do down and distance. We do stats. So we know stuff. We want to keep track of stuff.
I have a heavy track background. My dad was an Olympic track coach. I was around Olympians forever. And I’m at practice listening to splits, listening to times for reps, I’m listening to technical feedback for mechanics. And then, after a while during the summer, I’m seeing them run 9.7, and you’re like, ‘That makes sense now.’ If you don’t hit these different points within your 10-meter splits, 20-meter splits, 30-meters splits, you’re not going to achieve the end goal time. So within practice, within a rep, with your warmup, you have to start tailoring yourself and have an understanding what specifically you need to do to obtain a 9.7 because you’re measuring against the clock, right?
That same thing can be applied to football with analytics. Coaches have used GPS load to justify if a player is exhausted or done too much and how they go into recovery and how they plan their scripts and their practice plan. That’s high level. That’s way more objective than a subjective view of basically, ‘We ran around today and did this.’ Now we’re having hard data to justify that this guy, compared to this guy, is gassed. So we have tailored practice for this guy specifically but not the whole team—compared to not resting this guy and pushing the whole team and now we hurt this guy. And we really need him. So analytics and data has definitely helped out.
On how he coaches with data . . .
I coach as a performance coach but also as a receivers coach with analytics. Specifically, I’ve worked with a lot of the NC State football players in the area and some pro guys, definitely some other colleges and the HBCUs and some high-level high school recruits in the area. I put GPS units on my guys when we run. We start with running technique. It’s important to be efficient within route running so that you can hit every point on the field efficiently, right? No part of the grass is off-limits because you know how to move your body in the most efficient way to that point on the field.
[I look] at the technicalities of biomechanics. Now I’ll put that in front of video, and then we break it down there. But then we take that and put it on the field. We go from the track or another surface to the field. Now once it’s on the field, it needs to be applied within sport. And I use GPS units to [monitor] change direction, acceleration, deceleration acceleration, average speed, top speed, of course, and just [overall] load. So the quicker that our younger generation can understand it and take the ownership of it off the coaches, the higher level football will be in quicker. Breakaway is doing that right now.
On whether he would have used data had it been available earlier in his career . . .
As a young player, I would have had to be receptive to this information, and I might have just been ignorant to the fact that it was even around, or it was just so early on that it wasn’t around. But I think it would have definitely added a year or two onto my career, just knowing how to be more strategic in my preparation.
Right now, we’re in the NFL offseason. So prior to showing up for OTAs, I was very calculated as an athlete. It was a chance to really hone in on or own the thing that you really good at, really work on that craft. So, for me, I always had to work on my top-end speed. I could not show up to an NFL camp and be anything less than that. And that was important to me. So in order to either maintain or to increase my top end speed, we have to measure, we have to run, we have to be consistent, we have to, like, run to the point of exhaustion. Understand that that point and that wall is going to be pushed further the next time. But we have to know where that wall is to even push it further.
On the infusion of sensors in the sport . . .
Sports and football are getting there. We have sensors in our helmets. There are sensors in the ball. There are sensors in our shoulder pads. There are sensors now in our tights, our girdles, to see what strains or what muscles are activated in our legs and seeing how they are strained or stressed. Next up will be shoes, right? That would measure stride length, frequency, force—almost like a force plate in your foot. Next will be your hands and gloves. You have your visor being able to do some digital overlay. There’s a way to make this whole thing work.
On joining Breakaway Data . . .
When I first heard about the idea around Breakaway, I was sold immediately because I was like, ‘Excuse me. where the [heck] was this my whole career?’ I’ve been waiting to aggregate all my stuff in one place. It’s just been me by myself, and I cannot do it, and I need something to help me make it all sticky.
The part about it that is really cool is that it incorporates your recovery outside of football, your lifestyle outside of football, with your lifestyle within football. If I can figure out if I walked too much in the mall and figure out how much energy I would have for practice, it would definitely on the next day be like, ‘Don’t go walk around the mall that much.’ Without that, guys were doing this pregame ritual when you go to a new city—for instance, we would stay at the Galleria in Houston, and it was right across the street from a nice big designer mall. All I saw was just steps and steps that I need to recover from prior to tomorrow. They’re going to ask a lot of me, and I’m going to run a lot tomorrow. So let me not use that energy over there at the mall.
One of my guys [that I coach] is coming back from an injury. We’re not doing as many reps with him, but his GPS load and output and overall energy exertion was just as high as one of other guys. I’m like, ‘So what were you doing if you weren’t in the workout?’ Then you start looking back at the video, and he’s having fun behind the line. He’s moving over here. He’s dancing. He’s doing this, he’s doing that. And all that plays into it. I’m measuring it, and I’m getting it. I’m like, ‘Well, you need to just sit down somewhere. You need to be a little more detailed and fine- tuned.’
On empowering athletes through their data . . .
Breakaway is knocking on the door of owning that data. Own your data. The team’s tracking it for their own good, but it’s going to be on your own personal self to go take advantage of that and read it and understand it and ingest it and figure out how to become a better player yourself to even help your team. A football roster is 60, 70-plus in the NFL. Colleges are 100 deep. That’s a lot of bodies to assess. I have 12 players, and it takes me three hours to go through some lines of code and lines of data and statistics.
I cannot imagine doing a full team on the daily, but an individual player can go in read his line off in maybe 20 minutes—see what they did, how they did—and now they can justify their day after that. For linear speed, I like to see average sprint speed and average time spent sprinting. Now if you have a long time sprinting, now you’ve got to understand the mechanics of sprinting. You put a lot of high stress on those body parts. It’s like you’re a driver, and you need to go check the tread on your tires.
Sure, your tires are worn. You’ve got to go refresh your tires. The same thing happens to your body. You have to know what you’re doing—not just go and feel it, but you’ve got to know exactly what you’re doing and have a plan in place. And once you start justifying creating a schedule a routine that you know, satisfies your output, now you’re going to be a consistent Tom Brady type player, right, like longevity type stuff. We can’t just grind it out like we used to in old-school football. And that’s where the younger class has to get on board and be able to read what is being measured, but also understand who to go to and where to go to manipulate the outcome or output on the other side. So, that’s Breakaway.
On getting players to lean into data . . .
Building trust with data is tough, right? It’s almost like building trust with the court system. It’s got to be fair. No bogusness, no BS. It is for you, and it can be used against you. It’s a weapon and a shield. Some teams do a good job of taking care of their athletes, but the uniqueness of it is that it can be used as a shield to keep away the BS. A team is going to try to justify your reps, your pay, your position, your whatever, by whatever they collect. But you need to know, as if you were in courtroom, what they have on their side, to even combat it. And it has to be an open forum. And you can’t be intimidated by knowing that they know.
It’s just an understanding that it’s something that’s going to happen, like our iPhones are tracked but that doesn’t stop you from using your phone, right? We’re ahead of the game, in the sense of we are so early that every time we meet a person, we have to explain its usefulness or benefit. But you still need a place to put [the data]—so insert Breakaway and its app. It’s definitely why I’m here and why I’ve been so passionate in the space because as much as it’s annoying to try to get data released to us, it’s necessary. The sooner that all the data could be sent and delivered back to an athlete, the better, and it’ll lessen that feeling of being scared about the new technology.
It starts with owning your own shit. That was one of my slogans I pitched to Breakaway— own your shit. You have to own your stuff, like listening to the Panthers tell me it was I was running at 23 miles an hour. That was just the tip of it. That was just throwing a pebble in my pond, and the ripples started going. They’re cutting and signing people based off of expenditure of GPS loads and movements and all sorts of craziness. You need to know what they’re doing and why they’re doing it and what they’re tracking and why they’re tracking it, to even fight against that.
This article was brought to you by SportTechie, a Leaders Group company. As a Leaders Performance Institute member, you are able to enjoy exclusive access to SportTechie content in the field of athletic performance.
A data & innovation article 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 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.
