If you’ve spent any time digging into NBA box scores, prop bets, or the sudden rise of obscure benchwarmers in any franchise roster, chances are you’ve come across the name of a certain Koenrik Blomstaer. Born in Utrecht 36 years ago, and now splitting his time between Amsterdam and Boston, Koenrik is part sports journalist, and part data whisperer. His Master’s in Sports Analytics from Boston, combined with a Bachelor’s in Applied Statistics from the Universiteit van Amsterdam, he’s the guy NBA front offices should be listening to (and rumours say they are already doing it). Koenrik believes that stats don’t just tell stories, they can predict them. We sat down with him to talk about patterns, numbers, gut feelings, and why watching games still matters.
Q1. Koenrik, let’s start simple. What got you into data journalism, specifically in sports?
There were two things that made me do the move: my love for basketball and my obsession with patterns. I connected with Basketball in 2008, I was already a bit grown up, but it caught my interest when I saw players popping-up for 30 points out of nowhere. Data told me why that could happen.
Q2. Why basketball over, say, football or tennis?
Basketball is beautiful chaos. There are just so many possessions, so many decisions per minute. It generates much more data than other sports, so it’s a playground for analysts.
Q3. Is there a stat you think is massively overrated?
Points per game. It lacks context. Efficiency metrics matter much more, and so does usage. You can score 25 points on 30 shots and you would still have a miserable game, or the infamous triple-doubles Westbrook used to have… with seven turnovers.
Q4. And underrated?
Deflections. Seriously. These show activity, anticipation, disruption… but they rarely show up in highlight reels.
Q5. You once said “Data doesn’t lie, but you have to know how to listen to it.” Can you explain that?
Sure. Numbers reflect reality, but you have to be asking the right questions to use them properly. Cherry-picking data to fit narratives is not useful, at all. My job is to let data speak, not shout over it. It’s like jazz, there is a structure, but you have to listen for the nuances.
Q6. Do you think coaches listen to data enough?
Some do. The good ones are able to blend it with instinct. Others ignore it and then wonder why their rotations fail.
Q7. What’s one moment where the data completely changed your view of a player?
I think DeMar DeRozan. His midrange shots are a bit inefficient compared to three-pointers, but then I saw his late-game decision-making, the ability to draw fouls on him… the analytics didn’t hate him, they just needed better context, but saying that in TikTok is not that popular.
Q9. What’s your biggest win?
A $25 bet on Kyle Anderson to get a triple-double. It hit at 60-to-1. Still don’t know how.
Q10. Ever been burned badly?
Too many times. Betting against Westbrook during his MVP season? Rookie mistake. Never bet against chaos.
Q11. Do you code your own models?
Yes, most of them. Python, R, even a bit of SQL. I like building from zero, it gives me control.
Q12. What do you think of fans who say “the eye test” beats analytics?
I get it. The eye test is romantic. But our eyes lie. Data doesn’t have biases, but we do.
Q13. Any NBA player you think is criminally underappreciated?
Tyus Jones. He’s efficient, he doesn’t lose too many possessions, he’s solid on defense… he’s not flashy, but it’s a player the number-freaks like me love.
Q14. What’s a stat line that makes you go “yep, that guy’s underrated”?
Someone that scores 12 points, 8 assists, just a turnover… and most importantly, a +15 on/off. That’s a guy that makes you win games without needing the ball every possession.
Q15. Have any teams ever reached out to you directly?
Yes, of course, but I won’t disclose names. A couple of teams that reached the playoffs recently wanted a second opinion on matchup data.
Q16. Is there a stat you wish existed but doesn’t?
I think “potential spacing created”. Something that accounts for how a player affects defensive gravity, creating spaces for the rest of his teammates.
Q17. How do you balance storytelling and analysis in your writing?
I build stories through data, but without replacing it. You can’t just throw charts at readers. You need to walk them through it, like a coach on the sideline.
Q19. What advice would you give to young analysts trying to break into sports media?
Learning to code is important, but you have to learn to explain as well. If you can’t communicate your insights, they are useless. You should aim to have your name in certain websites, so you can connect with a lot of different people.