Should I Build It Should I Build It
07 Jun 2025
Sports

A website that analyzes historical sports data and provides ...

...predictions on winners and EV values for people looking to make bets

Confidence
Engagement
Net use signal
Net buy signal

Idea type: Swamp

The market has seen several mediocre solutions that nobody loves. Unless you can offer something fundamentally different, you’ll likely struggle to stand out or make money.

Should You Build It?

Don't build it.

Your are here

Your idea of a website analyzing historical sports data for betting predictions enters a crowded space. We found 24 similar products, indicating high competition. The "Swamp" category suggests existing solutions are mediocre, meaning you'll need a significant differentiator to succeed. Engagement is low across these similar products, with an average of only 1 comment per product. Neither net use nor net buy signals show any significant traction for these products. Given this landscape, standing out and achieving profitability will be challenging unless you can offer something genuinely unique and valuable that solves user needs better than existing solutions.

Recommendations

  1. Thoroughly investigate why current sports betting analytics platforms aren't resonating with users. Read user reviews, analyze their features, and identify unmet needs or pain points. Use the user criticisms from the similar products to your advantage!
  2. If you decide to proceed, narrow your focus to a specific niche within sports betting. Consider catering to a particular sport (e.g., eSports, niche international leagues), betting style (e.g., arbitrage betting, prop bets), or user segment (e.g., novice bettors, experienced analysts). Addressing a focused audience allows for more tailored and impactful features.
  3. Instead of directly competing with established sports data providers, explore opportunities to develop tools and services that enhance their offerings. This could involve creating API integrations, data visualization dashboards, or custom analytics models that integrate into existing platforms. You can make these providers your customers!
  4. Consider adjacent problems within the sports and entertainment industries that might offer more promising opportunities. For example, you could explore fantasy sports analytics, player performance tracking tools, or fan engagement platforms. These areas may have less competition and greater potential for innovation.
  5. Before investing significant time and resources, conduct thorough market research and validate your assumptions. Talk to potential users, gather feedback on your proposed features, and assess the competitive landscape. Based on the feedback from 'I made a news site built on prediction markets' launch, consider getting feedback early and often.
  6. Focus your efforts on creating truly novel prediction models or data visualizations. Differentiate yourself by leveraging unique data sources, advanced machine learning techniques, or innovative presentation methods that provide bettors with an edge.
  7. Prioritize building a strong community around your product. Engage with users on social media, create forums for discussion, and actively solicit feedback. Building a loyal community can help you attract and retain users in a competitive market.

Questions

  1. What are the critical unmet needs of sports bettors that existing analytics platforms fail to address, and how can your solution provide a significantly better user experience?
  2. How can you leverage unique data sources or prediction models to create a truly differentiated offering that stands out from the crowded sports betting analytics market?
  3. What specific strategies will you employ to build a strong community around your product and foster a loyal user base in the competitive sports betting industry?

Your are here

Your idea of a website analyzing historical sports data for betting predictions enters a crowded space. We found 24 similar products, indicating high competition. The "Swamp" category suggests existing solutions are mediocre, meaning you'll need a significant differentiator to succeed. Engagement is low across these similar products, with an average of only 1 comment per product. Neither net use nor net buy signals show any significant traction for these products. Given this landscape, standing out and achieving profitability will be challenging unless you can offer something genuinely unique and valuable that solves user needs better than existing solutions.

Recommendations

  1. Thoroughly investigate why current sports betting analytics platforms aren't resonating with users. Read user reviews, analyze their features, and identify unmet needs or pain points. Use the user criticisms from the similar products to your advantage!
  2. If you decide to proceed, narrow your focus to a specific niche within sports betting. Consider catering to a particular sport (e.g., eSports, niche international leagues), betting style (e.g., arbitrage betting, prop bets), or user segment (e.g., novice bettors, experienced analysts). Addressing a focused audience allows for more tailored and impactful features.
  3. Instead of directly competing with established sports data providers, explore opportunities to develop tools and services that enhance their offerings. This could involve creating API integrations, data visualization dashboards, or custom analytics models that integrate into existing platforms. You can make these providers your customers!
  4. Consider adjacent problems within the sports and entertainment industries that might offer more promising opportunities. For example, you could explore fantasy sports analytics, player performance tracking tools, or fan engagement platforms. These areas may have less competition and greater potential for innovation.
  5. Before investing significant time and resources, conduct thorough market research and validate your assumptions. Talk to potential users, gather feedback on your proposed features, and assess the competitive landscape. Based on the feedback from 'I made a news site built on prediction markets' launch, consider getting feedback early and often.
  6. Focus your efforts on creating truly novel prediction models or data visualizations. Differentiate yourself by leveraging unique data sources, advanced machine learning techniques, or innovative presentation methods that provide bettors with an edge.
  7. Prioritize building a strong community around your product. Engage with users on social media, create forums for discussion, and actively solicit feedback. Building a loyal community can help you attract and retain users in a competitive market.

Questions

  1. What are the critical unmet needs of sports bettors that existing analytics platforms fail to address, and how can your solution provide a significantly better user experience?
  2. How can you leverage unique data sources or prediction models to create a truly differentiated offering that stands out from the crowded sports betting analytics market?
  3. What specific strategies will you employ to build a strong community around your product and foster a loyal user base in the competitive sports betting industry?

  • Confidence: High
    • Number of similar products: 24
  • Engagement: Low
    • Average number of comments: 1
  • Net use signal: 9.4%
    • Positive use signal: 9.4%
    • Negative use signal: 0.0%
  • Net buy signal: 0.0%
    • Positive buy signal: 0.0%
    • Negative buy signal: 0.0%

This chart summarizes all the similar products we found for your idea in a single plot.

The x-axis represents the overall feedback each product received. This is calculated from the net use and buy signals that were expressed in the comments. The maximum is +1, which means all comments (across all similar products) were positive, expressed a willingness to use & buy said product. The minimum is -1 and it means the exact opposite.

The y-axis captures the strength of the signal, i.e. how many people commented and how does this rank against other products in this category. The maximum is +1, which means these products were the most liked, upvoted and talked about launches recently. The minimum is 0, meaning zero engagement or feedback was received.

The sizes of the product dots are determined by the relevance to your idea, where 10 is the maximum.

Your idea is the big blueish dot, which should lie somewhere in the polygon defined by these products. It can be off-center because we use custom weighting to summarize these metrics.

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