Lately, after having worked with one great LLM after the other, I keep asking myself—what can’t they do? Need a meal plan? Done. Want to design an app? Easy. Crack a joke? Too simple. Research a topic? Challenging, but possible. Scan through hundreds of websites? Effortless. But predict the future? Now that is uncharted territory! With the ICC Champions Trophy final just around the corner, it’s time to push the two latest LLMs – GPT 4.5 and Grok 3 – to their limits. Can they analyze team stats, player performances, and historical data to predict the next cricket champion? In this blog, I’ll put these models to the test with some intriguing prompts and see if they truly understand cricket—or if predicting the future is still beyond their grasp. Let’s start.
Table of contents
What’s the Challenge?
So far, I’ve used LLMs primarily for text generation, image generation, image analysis, and coding tasks that rely mostly on their training data rather than external tools like web search and deep research. But this time, I’m taking a different approach. I want to test how well LLMs can predict the winner of the ongoing ICC Champions Trophy by not just using their pre-training knowledge but also referring to the current data from the web and reasoning through it. I aim to check how effectively these models gather, analyze, and synthesize real-time sports data to make logical predictions.
For this experiment, I’ll be using OpenAI’s GPT 4.5 and x.AI’s Grok-3, two of the most advanced LLMs available today. Both models dominate the latest AI benchmarks in reasoning, efficiency, and multimodal capabilities, making them ideal contenders for this test.
Summary of the Predictions
Based on all the tasks that I gave the two models, here is a quick summary of their predictions for the upcoming ICC Champions Trophy finals:
Join our free course, GenAI: A Way of Life, and discover how to use AI like GPT-4.5 and Grok-3 to solve real-world challenges. If you want to look at the details of how these LLMs gave these predictions, read below.
Let the Predictions Begin with GPT 4.5 and Grok 3
As discussed above, both GPT 4.5 and Grok 3 models bring an incredible bag of web research and analysis features that are essential for the match prediction. I will now ask the two models to predict the:
- Winning team
- Player of the match
- Winning total
- Toss to match winning probability
- Dream 11 team
Let’s see if GPT4.5 & Grok 3 can be our go-to match Pundits or not.
1st Prediction: Pick the Winning Team
Cricket experts around the world are picking their winning team based on their knowledge of the match, pitch, team performance and intuition. In this task, I want to see if GPT4.5 and Grok 3 can pick a favourite.
Prompt: “Predict one winner for the ICC Champions Trophy 2025”
GPT 4.5 Response:
Grok 3 Response:
Analysis:
| Aspect | GPT-4.5 | Grok 3 |
|---|---|---|
| Response Summary | Well-reasoned and balanced analysis. | Confident prediction but lacked depth. |
| Strengths | Referenced standout performances (Kohli, Williamson) and team strengths. | Highlighted India’s dominance and New Zealand’s balanced squad. |
| Weaknesses | Slightly favored India but provided clear rationale. | Failed to incorporate past match stats or head-to-head comparisons. |
| Data Accuracy | Accurate and diplomatic. | Incorrectly included Jasprit Bumrah, raising concerns about data accuracy. |
Verdict:
India is winning the ICC Champions Trophy 2025!
2nd Prediction: The player of the match
Choosing the player of the match is quite difficult as on a given day it’s hard to predict which player might just be the game changer. But LLMs rely on data and often it’s the data that holds the key to the future.
Prompt: “Predict the man of the match for the ICC champions trophy 2025 final.”
GPT 4.5 Response:
Grok 3 Response:
Analysis:
| Aspect | GPT-4.5 | Grok 3 |
|---|---|---|
| Response Summary | Well-rounded analysis, listing key performers from both teams and their strengths. | Assertive, with Virat Kohli as the top pick for Player of the Match. |
| Key Players | Virat Kohli and Mitchell Santner (calculated prediction based on statistical potential). | Virat Kohli, Hardik Pandya, Varun Chakravarthy (India), and Kane Williamson (New Zealand). |
| Reasoning | Diplomatic stance, picking one player from each team. | Structured and data-driven, outlining key deciding factors. |
| Strengths | Balanced analysis with a focus on statistical potential. | Backed choices with performance trends and clear reasoning. |
| Weaknesses | Santner’s inclusion based more on potential than recent performance. | None explicitly mentioned, but could be seen as overly assertive. |
Verdict:
Both models leaned towards Virat Kohli as the standout performer. However, GPT 4.5 played it safe with a balanced choice, while Grok 3 took a more confident, data-backed approach with additional contenders.
3rd Prediction: The Winning Total
The winning total is the score that the team batting first should aim to achieve to be able to win the match. Predicting a total is comparatively simpler as it is often based on the matches that have been previously played on the given ground. This is where LLMs can perform better than most humans.
Prompt:” Based on all the matches that have been played on the final ground, what would be the winning total that the team batting first should aim for in the ICC Champions Trophy Finale?”
GPT 4.5 Response:
Grok 3 Response:
Analysis:
| Aspect | GPT-4.5 | Grok 3 |
|---|---|---|
| Response Summary | Concise yet insightful, referencing past high scores and successful chases. | Mathematical approach, using historical data and calculations. |
| Key Insights | Referenced India’s recent semi-final win against Australia, where they chased 264 comfortably. | Conducted calculations, including solving a quadratic equation, to determine the likely winning score. |
| Prediction | Predicted a competitive winning total of 280-300. | Predicted a winning score between 250-260. |
| Strengths | Insightful analysis based on recent match performances. | Detailed mathematical approach using historical data. |
| Weaknesses | None | Overlooked recent match performances, which could have refined the prediction. |
Verdict:
Overall, GPT 4.5 balances theory with practical insights, incorporating recent trends to arrive at a logical estimate. Grok 3 relies heavily on mathematical modeling but misses real-world context. While 280-300 seems like the safer target, Grok 3’s lower estimate could prove valid under final-match pressure.
4th Prediction: The Toss to Match Winning Probability
Often the toss plays a crucial role in determining the match winner. A favourable result at the toss allows a team to make a crucial decision about the flow of the match. While a toss win doesn’t guarantee a match win, yet there is some tangible relation between the two. Let’s see if the LLMs can read between the lines of this relation.
Prompt: “What is the win probability if a team wins a toss based on the previous matches that have been played on the ground where the ICC Champions Trophy finale is taking place? Explain the entire stats with proper charts.”
GPT 4.5 (Deep Research) Response:
By Grok 3 (Deep Search) Response:
Analysis:
| Aspect | GPT-4.5 | Grok 3 |
|---|---|---|
| Response Summary | References historical data but shifts focus to analyzing bat/bowl decisions instead of direct win probability calculations. | Structured approach with detailed probability calculations and visual representations (pie charts, bar graphs). |
| Key Insights | Provides inaccurate statistics on toss-to-win correlations and assigns a 50% probability of winning after a toss win. | Concludes that teams winning the toss have a 47.5% chance of winning, acknowledging minimal influence on the final result. |
| Strengths | Attempts to analyze the impact of choosing to bat or bowl first. | Detailed calculations, tabular breakdowns, and visual representations of data. |
| Weaknesses | Inaccurate statistics, ambiguous 50% probability, and failure to address the original query directly. | Acknowledges minimal toss influence, which may downplay the significance of its calculations. |
Verdict:
Overall, both models agree that the toss does not significantly impact match outcomes, with win probabilities hovering around 50%. While GPT-4.5 deviates from the core question by analyzing batting-first versus bowling-first strategies, Grok 3 delivers a more relevant, data-driven response.
5th Prediction: The Dream 11 Team
Dream11 lets users create a fantasy cricket team by selecting 11 players they believe will perform best in a match. Players earn points based on their real-game performance, with the captain and vice-captain receiving extra points. The user with the highest-scoring team wins a cash prize. Since I want to maximize my chances of winning, I’ll use LLMs to predict the best team. Let’s see what they can do!
Prompt: “Create the dream 11 team for the ICC Champion Trophy final.”
GPT 4.5 Output:
Grok 3 Response:
Analysis:
| Aspect | GPT-4.5 | Grok 3 |
|---|---|---|
| Response Summary | Astute and balanced, with a focus on recent player performance and traditional choices for captain and vice-captain. | Biased toward Team India, with an aggressive and risk-oriented approach. |
| Team Selection | Balanced mix of best players from India and New Zealand. | Majority of Indian players, including Jasprit Bumrah (incorrect selection). |
| Captain & Vice-Captain | Traditional choice: high-performing, in-form batsmen. | Risk-oriented: all-rounder and bowler as captain and vice-captain. |
| Strengths | Prioritizes consistency and relies on recent player performance. | Values potential match-changers and takes calculated risks. |
| Weaknesses | Sticks to traditional choices, avoiding unexpected gambles. | Significant error in team selection (Jasprit Bumrah) and noticeable bias toward India. |
Verdict:
Overall, some key players remain common between the Dream 11 team designed by GPT4.5 and Grok 3. Although for this prediction, I preferred GPT-4.5’s team since its team seemed more balanced and all its choices are performance backed.
Conclusion
LLMs like GPT 4.5 and Grok 3 analyze data and trends for predictions, but their accuracy hinges on data quality and reasoning. GPT 4.5 offers balanced insights, while Grok-3 takes riskier, sometimes flawed approaches. AI can aid sports forecasting, but expert verification is key. AI enhances decisions but can’t replace human judgment – factors like injuries and strategy changes remain unpredictable. For now, GPT-4.5 is reliable and conservative, while Grok 3 is bold but riskier. As AI evolves, sports predictions may grow even more precise!
Enroll in our free course, GenAI: A Way of Life, and learn how to leverage AI like GPT 4.5 and Grok 3 in real-world scenarios.
Frequently Asked Questions
Q1. What is the ICC Champions Trophy?
A. The ICC Champions Trophy is a One Day International (ODI) cricket tournament organized by the International Cricket Council (ICC) every four years, featuring the top eight ranked teams.
Q2. Which teams reached the ICC Champions Trophy 2025 final?
A. India and New Zealand qualified for the final. India defeated Australia in the semifinals, while New Zealand defeated South Africa.
Q3. Which AI models were used for match predictions?
A. OpenAI’s GPT-4.5 and x.AI’s Grok 3 were used to predict the winner, player of the match, winning total, and Dream11 team.
Q4. What factors did the AI models consider for predictions?
A. The models analyzed recent player performances, historical match data, team balance, and pitch conditions using live web search and deep reasoning.
Anu Madan is an expert in instructional design, content writing, and B2B marketing, with a talent for transforming complex ideas into impactful narratives. With her focus on Generative AI, she crafts insightful, innovative content that educates, inspires, and drives meaningful engagement.
