Why Opening and Closing Lines Matter in Betting And How My Model Beats Both
Understanding the difference between opening and closing lines and why my model outperforms both
In the world of sports betting, understanding the difference between an opening line and a closing line is key to becoming a sharper bettor.
- The opening line is the first version of the odds that a sportsbook releases. It's set based on internal models, early market sentiment, and projected player performance.
- The closing line is the final price just before the game starts and it's typically the most accurate. By this point, sportsbooks have adjusted for betting volume, injury news, starting rosters, and any other available information. Beating this number consistently is extremely difficult, even for professionals.
📊 Real Example: How Lines Move in Esports
Take Faker's map 1 kill prop. PrizePicks might set the opening line at 3.5 while Underdog sets it at 4.5. +EV bettors using optimizers immediately slam PrizePicks OVER 3.5 and Underdog UNDER 4.5, causing both closing lines to settle at 4.0. But these tools only see market discrepancies. They don't know Faker historically averages 4.2 kills against this specific opponent. Only one of their two bets is actually +EV.
The other major reason lines get bumped is when betting influencers (cappers) post their picks and all their followers slam the same line. This influx of volume on the same side is what triggers the bookmaker to adjust the number. Usually only a couple thousand dollars worth of bets is enough.
Here's the difference: these influencers typically bet off feel without a clear methodology. +EV optimizers only look at market discrepancies without understanding the game or players.
My model sees all of Faker's historical games, the enemy team's entire historical performance, and even factors in the ML odds of which team is likely to win. Using this incredibly detailed information, it creates projections that are usually much sharper than the market and its participants.
So what does this have to do with the LCSLarry x Juiced Bets Esports Model?
Everything.
I've built a model that not only crushes the opening line, but also outperforms the market at the closing line. Something that most betting tools can't claim.
📈 Opening Line Performance: Unreal Results
If you start using the model, it'll become incredibly obvious that most opening lines are extremely mispriced.
When I track how my model performs against opening lines, the results are ridiculous. Over thousands of tracked bets, the model is delivering a ROI of 56% and a win rate of 63% (pulled directly from lcslarry.com).
That's the kind of edge that experienced bettors dream about. And it proves that my model's projections are consistently ahead of the market.
🕒 What If You Miss the Opening Line?
I get it. Not everyone can bet right when lines drop. Life happens, and sometimes you aren't able to place bets right as projections release.
Here's the good news: you can still tail profitably. Unlike traditional sports where closing lines are nearly impossible to beat, esports markets remain soft enough that betting closing lines can still be profitable (with the right model).
When a betting influencer posts picks and their followers slam the same line, that volume moves the number. But the underlying value often remains because they don't always move the line in the right direction.
Even when I compare my results against closing lines, where all public information is supposedly priced in, the model continues to win. In fact, it maintains a positive ROI even against the most efficient line possible.
That's nearly unheard of.
Most sharp bettors measure success by how often they get "closing line value" (which basically means how often they bet on the opening line before it gets bumped), not by whether they make money after the closing line. But my model? It does both.
🧠 Beating the Closing Line = Beating the Market
To beat the closing line with consistency is rare. To show a positive ROI betting at the closing number is insane. Why?
Because the closing line reflects the collective wisdom of the market. The sharpest minds, biggest bankrolls, and most accurate data have all contributed to shaping it. If you're winning even after that line has settled, it means your model has found true inefficiencies that even the market can't price correctly.
But here's why this is possible in esports: we're dealing with a fundamentally different market structure than traditional sports.
🏆 The Esports Advantage
Esports betting is where NBA betting was 20 years ago - inefficient and beatable. Fewer people understand the games, books don't prioritize model development, and the market lacks the institutional money that makes traditional sports nearly impossible to beat long-term.
While NFL, NBA, and MLB have billions invested in extremely sharp bookie models, esports remains a relatively new market with softer lines. Not many books even offer esports betting, and those that do often don't care enough to refine their models. Less betting volume, fewer sharp players, newer market. This creates massive opportunities for sharp bettors who understand the games and can leverage the data correctly.
That's exactly what I've done with my esports model.
Thanks to deep knowledge of the game and the power of clever data engineering, advanced mathematics, and machine learning, I've built a model with its own unique edge. I can't spill the secret sauce here, but you can trust that my projections are extremely sharp.
My track record speaks for itself. I've been consistently making money in esport markets for over three years with a 50% ROI all time. All results are tracked transparently on lcslarry.com and updated daily. I don't hide anything.
Bottom Line
Yes, it's always optimal to get your bets in early. That's where you'll find the biggest edges. But even if you're late to the party, my results show that you're still likely to be profitable.
If you want to tail a model that beats the market from open to close, check out my full performance and access the picks at https://whop.com/esports-model/.