Eric Gagne was one of the best relievers in the MLB. In his 10-year career, he made the All-Star team in three straight seasons from 2002-2004, finished with the fourth-highest save total in a season with 55 and won a Cy Young Award for his 2003 season — he’s one of just nine relievers to win the award in MLB history.
Gagne was as dominant as they come, and played in Los Angeles for the first eight years of his career. His MLB career ended in 2008 — although he did attempt a comeback with the Dodgers in 2010 — right before what felt like the drastic shift to analytics-based baseball. That’s why, when he joined the Blue Heaven Podcast here at Dodgers Nation, we asked him about analytics, and how different it is today than it was when he played. He shared a very interesting perspective that could change your thoughts on analytics as a whole:
“It’s still the same. I think we’re just gonna use different words, we use numbers. I think if you really sit down and understand the numbers, which I love now. I mean, I love numbers but they’re just numbers. I think that the beauty of baseball is those variables — the human factor. It’s not black and white we all know that. If we watch the game, it’s not black and white, it’s gray. There’s a lot of gray — it’s the variables of the decision-making process and what’s going to happen with certain guys. You know, this is what’s beauty in the game.”
So despite what we may feel to be a “drastic shift” in an analytical approach, Gagne actually said it’s pretty much the same than when he played. There may be different statistics and more data, but at the same time, it’s still a similar approach through numbers. Here’s what he said about when analytics becomes a problem, and how to avoid that issue:
“I think the problem is not just in data and the analytics, it’s more of how we look at it. If you look at it as a pure game, yeah, there’s a lot of variables. You can add like a spin rate and everything else. That doesn’t matter much because spin rate was there before. There’s no difference. Now, we just put numbers on it and that’s all it is. It’s just a tool to use. You just utilize that tool for coaches, for players to see, ‘okay, where am I at when I performed the best.’ It has changed but it’s okay. It needs to change its called evolving and it’s okay, it’s okay to change. As long as you don’t lose the beauty of this game — the human nature, the human factor, the little decision-making we have to make in certain situation. Because there’s no way I can predict the game because it wouldn’t be a same game. … I think in the whole grand scheme of thing data analytics is very good for the game. If you use it the right way.”
That last part is definitely the key: “If you use it in the right way.” Sometimes it does feel like teams are leaning too heavily on analytics, and less on the human nature, personal feel of the game. Many have questioned the Dodgers’ decision-making in the postseason, especially in regard to taking pitchers out too early. That feels like the biggest area where the analytics are becoming a problem.
However, the Dodgers are clearly doing something right, as they lead the league in winning percentage since Dave Roberts’ first season in 2016. The Dodgers have a winning percentage of .632 in that time, while the Astros are second at .606. The Dodgers just have to find a way to translate that into more postseason success — and hopefully, 2023 is the year.
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