Grok 4’s Bold Call: Dodgers to Win 2025 World Series—But Competing AI Models Disagree
AI vs. AI: The World Series prediction wars heat up as Elon's Grok 4 goes all-in on the Dodgers—while rival algorithms hedge their bets.
Who's right? The machine that picked last year's champion... or the ones that didn't?
Meanwhile, Wall Street quietly adjusts its 'AI Sportsbetting ETF' allocations—because nothing screams 'efficient markets' like neural nets gambling with other people's money.
What other AIs think
ChatGPT's o3 model gave the Dodgers a 26% chance while flagging them as overpriced. The model identified Detroit as offering the best value with a 16% win probability against market odds implying just 12.5%. Its reasoning centered on Tigers ace Tarik Skubal's dominance and the team's league-best pitching staff.
DeepSeek doubled down on Los Angeles with a 23% probability, but noted the Dodgers might be riding too much positive sentiment. Despite favoring LA to win, the model said it WOULD rather bet on the Phillies because the risk-to-reward ratio was more compelling.
Since we're poor and our paymasters were unlikely to approve Grok 4 Heavy's $300 subscription for just one question, we asked the lighter Grok 4 version available via the $30 tier. Interestingly, it gave the Tigers a razor-thin edge over the Dodgers—less than one percentage point separated their odds.
All three models flagged similar factors: Detroit's elite pitching rotation, the Dodgers' injury concerns, and historical patterns suggesting the market overvalues defending champions.
It's all in the prompt
While Grok 4’s “Heavy” reasoning is impressive, you don’t need a $300/month plan to get solid predictions. With smart prompting, even basic models can deliver sharp insights. We found that successful prompts need at least these three main elements:
First, role-play. Tell the modelit should be andit should act. Try something like: "You are an expert Prediction Market Analyst with DEEP knowledge of Bayesian forecasting and risk management."
Second, the methodology: Tell the modelyou want andsteps it should follow in order to succeed. Ask the model to gather current betting odds from multiple sources, compare them against analytical projections, and identify value bets. Models perform better when they can compare market consensus against their own calculations.
This is what prompt engineers call Chain-of-Thought—if the model knows exactly what to do, it provides better results. Don't know how to guide it? Ask the model separately for the steps needed to complete your task.
Third, point toward analytical resources. Mentioning Baseball-Reference simulations or FanGraphs projections helps ground predictions in established frameworks, rather than pure speculation.
For those interested in trying this themselves, we built a custom GPT that replicates what xAI demonstrated with Grok 4. It was just a fun experiment, but it gathers odds, analyzes team performance, and identifies potential betting value through natural conversation.
We also tossed our prediction market prompt on GitHub if you want to experiment with your own chatbot.
Use at your own risk, naturally. We're not financial advisors, and neither are these AIs. If you lose, don't blame us—but if it helps you win big, then we won't say no to a beer.
