It is with great pleasure that we have brought back the Notinhalloffame MLB Regular Cup, and let us explain how this works:

For every regular-season game, we anointed the top five players with the most points, in descending order: 5-4-3-2-1. 

We know the following:

• The top players for the MLB NIHOF Cup are not always the best in the league, as injuries keep players out of games, and a premium on staying healthy can help pile up points. It also does not hurt to be a top player on an average or mediocre team, as they can amass Cup points more easily than elite players on loaded squads.
• In Baseball, it is more common than in Basketball and Hockey for a player to accrue points with a single Home Run in a game, which favors position players. Starting Pitchers have a hard time with approximately 30-35 Starts and throw fewer innings than previous generations. This is also true for closers not made for this process.
• Please remember that this is NOT necessarily who we think were the best players this year and does not reflect overall consistency. Treat this the way we did: as a fun process and more of a compilation of temporary statistical domination.
• As such, expect it to take time to see Pitchers on this list, or high-average hitters with limited power.

Here are the final standings (and note that we will be adding more of the results over the next few weeks):

1. Shohei Ohtani, Los Angeles Dodgers, Designated Hitter & Pitcher:  220 Cup Points in 158 Games, 1.39 Cup Points per Game.   7.7 bWAR, 146 Runs Scored, 164 Hits, 55 Home Runs, 102 Runs Batted In, 20 Stolen Bases, .282/.392/.622 Slash Line, 1.014 OPS & 179 OPS+.  14 Games, 1-1 Record, 2.87 ERA, 47.0 IP, 62 SO, 145 ERA+, 1.043 WHIP, 6.89 SO/BB.

Who other than a superstar who can accrue points with his bat and on the mound, win the Notinhalloffame Cup?

This is a trophy built for Shohei Ohtani, the only active player in the Majors who plays both ways. Although he threw for only 47 Innings, that is what put him over New York’s Aaron Judge.  Ohtani led the National League in Runs (146), Slugging (.622), OPS (1.014), OPS+ (1.014), and broke his single-season Home Run record with 55 dingers. 

The final week in the standings was a battle between Ohtani and Judge, and while his work as a hurler put him over the top, what Ohtani has done this year and since 2021 has been nothing short of immaculate.

Congratulations to Shohei Ohtani for winning the notinhalloffame.com MLB Cup. 

By the way, the title needs to be accepted in person here at our current home base in Seattle.

2. Aaron Judge, New York Yankees, Outfield:  217 Cup Points in 152 Games, 1.43 Cup Points per Game.  9.7 bWAR, 137 Runs Scored, 179 Hits, 53 Home Runs, 114 Runs Batted In, 12 Stolen Bases, .331/.457/.668 Slash Line, 1.114 OPS & 215 OPS+.

Aaron Judge had the Notinhalloffame Cup locked up, but after bouncing back and forth with Shohei Ohtani, Judge fell in the last two games. However, it is hard to beat a player (for this Cup) when you don’t pitch.  Wait, does that mean Judge is the de facto winner here?   Sadly, no.

Judge had a phenomenal year, where he maintained his power (53 Home Runs) while winning his first Batting Title (.331).  He did not just lead the AL in that stat; he swept the Slash Line, OPS, and OPS+ while also finishing first in Runs (137) and Walks (124).   

The Yankees made it to the playoffs, but could they have done so without Judge?  We doubt it.

3. Cal Raleigh, Seattle Mariners, Catcher:  183 Cup Points in 159 Games, 1.15 Cup Points per Game.  7.3 bWAR, 110 Runs Scored, 147 Hits, 60 Home Runs, 125 Runs Batted In, 14 Stolen Bases, .247/.359/.589 Slash Line, .948 OPS & 169 OPS+.

Is this the best year by a Catcher?  Offensively, yes, it looks like!

Raleigh shattered the Home Run record for a Catcher with 60 taters, and led the AL in that stat and RBIs (125).  “The Big Dumper” was an All-Star for the first time in 2025, and his output propelled the Mariners to a top seed in the 2025 playoffs. 

4. Pete Alonso, New York Mets, First Base: 179 Cup Points in 162 Games, 1.11 Cup Points per Game.  3.4 bWAR, 87 Runs Scored, 170 Hits, 38 Home Runs, 126 Runs Batted In, 1 Stolen Base, .272/.347/.524 Slash Line, .871 OPS & 144 OPS+.

Would you believe that Alonso was at the top of the standings (by far) when we first published our ranking in early May? 

This is arguably the first surprise on this list, as, with all due respect to Alonso, he does not seem like he should be this high, but again, we remind you that this is a point system based on individual games! 

Alonso had a great year, blasting away like always, but this time with a respectable Batting Average of .272 (his best), and a National League leading 41 Doubles. 

Regardless, the biggest news for Alonso is that he opted out of his contract and will likely not be a Met next year.

5. Jose Ramirez, Cleveland Guardians, Third Base:  166 Cup Points in 158 Games.  1.05 Cup Points per Game.  5.8 bWAR, 103 Runs Scored, 168 Hits, 30 Home Runs, 85 Runs Batted In, 44 Stolen Bases, .283/.360/.503 Slash Line, .863 OPS & 137 OPS+.

The story of the improbable Guardians' run to the postseason can not happen without their top gun, and potential Hall of Famer, Jose Ramirez, who added his seventh All-Star and fifth straight.  He was fourth in OPS+, sixth in OPS, and eighth in Slugging.

6. Juan Soto, New York Mets, Outfield: 164 Cup Points in 160 Games, 1.03 Cup Points per Game.  6.2 bWAR, 120 Runs Scored, 152 Hits, 43 Home Runs, 105 Runs Batted In, 38 Stolen Bases, .263/.396/.525 Slash Line, .921 OPS & 160 OPS+.

It is the New York Mets that are the first team to post two players, and it comes in the form of a player who had a slow start after signing a monster contract.

Juan Soto did not make the All-Star Game (making him the highest-ranked player on this list not to), but he finished the season as the National League leader in OBP (.396), Walks (127), and Stolen Bases (38), the last of which was a huge surprise considering his previous high was 12.  Soto also had a career-high 43 Home Runs.  The Mets may not have made the playoffs, but in year one, New York got value from the superstar.

7. Francisco Lindor, New York Mets, Shortstop: 163 Cup Points in 160 Games, 1.02 Cup Points per Game.   5.8 bWAR, 117 Runs Scored, 172 Hits, 31 Home Runs, 86 Runs Batted In, 31 Stolen Bases, .267/.346/.466 Slash Line, .811 OPS & 129 OPS+.

Yes.  The New York Mets, the team with the most epic choke job in the last twenty years, have three ranked players before any other squad has two.  How is this possible?  The short answer is to see how long it takes for the Mets to have five players here, and when a Pitcher finally shows up.

Lindor had his first All-Star since 2019 (fifth overall), and was the NL leader in Plate Appearances (732) and At Bats (644).  He also had his second 30-30 year, and was third in Runs Scored (117), fifth in Hits (172), and was eighth in Home Runs (31).

8. Manny Machado, San Diego Padres, Third Base: 162 Cup Points in 159 Games, 1.02 Cup Points per Game.   4.1 bWAR, 91 Runs Scored, 169 Hits, 27 Home Runs, 95 Runs Batted In, 14 Stolen Bases, .275/.335/.460 Slash Line, .795 OPS & 118 OPS+.

Machado continues his amazing career by adding a seventh All-Star and continuing to be the Padres' top offensive weapon.  Machado, who was ninth in Hits in the NL, also turned a National League-leading 34 Double Plays at Third Base. 

9 (TIE). Kyle Schwarber, Philadelphia Phillies, Designated Hitter: 161 Cup Points in 162 Games, 0.9938 Cup Points per Game.  4.7 bWAR, 111 Runs Scored, 145 Hits, 56 Home Runs, 132 Runs Batted In, 10 Stolen Bases, .240/.365/.928 Slash Line, .928 OPS & 150 OPS+.

This year’s All-Star Game MVP led the NL with 56 Home Runs and 132 RBIs, both of which were career highs.  He also had a career best in Hits (145), and was second in both Slugging and OPS, but his 197 Strikeouts cost him Cup Points. 

9 (TIE). Junior Caminero, Tampa Bay Rays: 155 Cup Points in 146 Games. (#6 Last Week).  4.2 bWAR, 89 Runs, 149 Hits, 44 Home Runs, 108 Runs Batted In, .259/.302/.537 Slash Line, .839 OPS & 128 OPS+.

This was the (expected) breakout year for the 22-year-old Dominican Third Baseman, who exploded with 45 Home Runs, a .846 OPS, and the best bat on a promising Rays roster.  We can’t wait to see what “La Maxima” has next!

Soon, we will release updates that will show the complete list.

In a digital era where data drives every punt, the demand for verifiable, real-time information in Horise racing news has never been greater. Modern punters don’t just follow the races; they dissect sectional times, evaluate trainer profiles, and analyse form guides before placing a wager. The need for precision, clarity, and accountability underpins the evolution of the racing industry — and it’s here that Horise sets a new standard for information transparency and user trust.

As a specialised hub for horse racing news, racing calendars, and global horse racing profiles, Horise has built its reputation on accuracy, accessibility, and expert-level analysis. By combining multilingual accessibility, advanced data validation processes, and community-driven refinement, Horise ensures that the sport’s most important currency — trust — is never compromised.

Why Transparency Matters in Racing

Horse racing has long been described as “The Sport of Kings,” but in Australia, the phrase carries a modern twist. Beneath the glamour of The Cup and the roar of the grandstand lies an industry sustained by data integrity. Punters rely on timely, accurate insights to make informed bets — from an each-way bet on a short odds favourite to a flutter on a roughie that might lob in at twenty-to-one.

Transparency matters because:

• It underpins credibility: Reliable race data reinforces trust between punters, bookies, and organisers.
   
• It levels the playing field: Verified data ensures casual fans and professional analysts alike access the same facts.
   
• It enhances engagement: When results, odds, and profiles are accurate, fan participation increases across betting platforms.
   
• It safeguards fairness: Data transparency mitigates manipulation, biased reporting, or odds distortion before the tote closes.
   

As one industry analyst summarised:

“In modern racing, transparency isn’t a courtesy — it’s the lifeblood of fan trust and industry sustainability.”

Without it, punters risk becoming mug bettors — misled by unreliable feeds, inconsistent race timings, or incomplete form guide data.

Common Data Gaps and Challenges

Despite advancements in analytics and broadcasting, global horse racing still suffers from information fragmentation. Racing authorities, bookies, and data providers often operate in silos, leading to inconsistencies that frustrate punters and distort betting markets.

Typical data challenges include:

• Inconsistent time tracking: Variations in sectional timing methods between racecourses.
   
• Fragmented racecourse information: Local meets lacking integration into broader international horse racing coverage.
   
• Delayed updates: Race results or steward reports posted hours after a meet concludes.
   
• Unverified statistics: Unofficial platforms publishing speculative trainer profiles or outdated jockey profiles.
   
• Lack of standardisation: Discrepancies in how performance metrics and track conditions are categorised.
   

Such fragmentation can turn even seasoned punters into blind bettors — betting on instinct rather than evidence. For a sector increasingly dependent on digital engagement, that’s a significant risk.

How Horise Ensures Accuracy

Horise counters these industry pitfalls through a data-driven, multi-tiered verification process. Rather than aggregating unvetted data, the platform operates with journalistic rigour and technological precision — effectively combining the reliability of an official form guide with the dynamism of a digital news network.

Horise’s accuracy framework is built around three core pillars:

1. Source Verification
    
   • Race data is sourced directly from official racing authorities and steward reports.
      
   • Video analysis and sensor-based time tracking confirm on-track performance metrics.
      
   • Independent audits ensure the elimination of false or duplicate entries.
      

2. Cross-Referencing Systems
    

• Advanced algorithms compare race data against multiple feeds for consistency.
   
• In cases of discrepancy, the Horise editorial team flags entries for manual review.
   
• Data is published only once alignment is verified across primary and secondary sources.
   

3. User Transparency and Feedback
    

• Every article or race profile includes a timestamp and verification badge.
   
• Users can report suspected inaccuracies, triggering an immediate audit.
   
• Continuous improvement loops ensure content evolves with real-world performance data.
   

These layers guarantee that Horise’s coverage — whether local meet updates or international event recaps — remains an authoritative reference point for both casual punters and industry professionals.

Reliable Profiles and Verified Results

At the heart of Horise’s transparency mission lies its database of meticulously verified horse racing profiles, trainer profiles, and jockey profiles. Each entry is dynamically updated to reflect changes in form, performance, and racing conditions.

Data integrity matrix:

These features are supported by multilingual content delivery — English, Chinese, Japanese, French, Spanish, and Arabic — extending Horise’s transparency ethos beyond Australia’s borders to truly international horse racing coverage.

Moreover, Horise’s racing calendars and live result streams eliminate time lags common in third-party platforms. This immediacy transforms user engagement, allowing punters to evaluate results and make informed decisions faster than ever before.

“Horise doesn’t just report results — it explains them. Every number tells a story, and every story is grounded in evidence.”

Conclusion: Trust Through Information

In an industry where margins are fine and timing is everything, information is the ultimate advantage. Transparency transforms punting from speculation into strategy. Whether a punter is staking a modest each-way bet or committing serious plonk on a dead cert, confidence in data is what separates calculated risk from blind chance.

Horise’s commitment to trust and transparency manifests in:

• Accurate and timely horse racing news verified by multi-source data.
   
• Comprehensive horse racing guides that educate punters with tactical insights.
   
• Up-to-date racing calendars ensuring no major meet slips under the radar.
   
• A multilingual, user-focused interface that delivers inclusivity and ease of access.
   
• A responsive support team that treats every query as part of its ongoing improvement process.
   

As global audiences continue to flock to digital platforms for racing analysis, Horise stands as the benchmark for transparency and reliability. It’s where reliable racing information meets usability, and where data integrity drives the sport forward — track by track, meet by meet, punter by punter.

For those who live by the form, chase the thrill, and respect the data, Horise remains not just a source — but the standard.

For as long as American football has existed, fans, analysts, and coaches have tried to predict its outcomes. From locker room debates to sophisticated analytics, the pursuit of foresight has always been part of the sport’s DNA. But as technology evolves, a new kind of strategist has joined the game — artificial intelligence (AI).

The question today is no longer whether AI can analyze the game, but whether it can truly predict it. Can data models anticipate a fumble, a defensive breakdown, or a clutch touchdown drive before they happen? Can algorithms decode a sport built on both order and chaos?

Surprisingly, the answer might be closer to yes than ever before.

The Complexity of Predicting Football Outcomes

American football is one of the hardest sports in the world to predict. Every play is a symphony of moving parts — 22 players, multiple formations, shifting weather conditions, and an endless list of situational variables.

Unlike basketball or baseball, football doesn’t provide a large statistical sample. NFL teams play only 17 regular-season games, meaning a single turnover or missed kick can define an entire year. Add to that the psychological factors — motivation, rivalry pressure, crowd influence — and prediction becomes more art than science.

Yet, this complexity is exactly what makes football an ideal testing ground for AI.

How Artificial Intelligence Approaches the Game

AI systems don’t rely on intuition or narrative. They rely on data. Using machine learning, models can analyze millions of data points: player speed, positional maps, play tendencies, injury history, and even fatigue patterns.

By comparing thousands of historical plays, AI can identify hidden correlations. For instance, a model might learn that a quarterback’s release time is a stronger indicator of success than passing yardage, or that teams using heavy formations on third down convert more often against specific defenses.

These are relationships that humans might never notice on their own — but AI can.

The concept isn’t entirely new. In global football (soccer), platforms like the NerdyTips platform have already shown how artificial intelligence can process vast datasets to forecast match outcomes and performance trends. While NerdyTips focuses exclusively on the world’s most popular sport, its success demonstrates how AI can turn raw data into actionable insight — a principle that can be adapted for American football as well.

AI’s Quiet Entry into the NFL

Artificial intelligence is no longer a futuristic idea within the NFL. It’s already part of the league’s ecosystem — quietly shaping how teams train, scout, and strategize.

Each player wears a tiny RFID chip inside their shoulder pads. These chips track positioning, acceleration, and separation on every play. That data feeds into AI-driven analytics platforms that provide coaches with unprecedented insights.

Teams use these systems for:

• Game preparation: Simulating thousands of play scenarios based on past tendencies.
• Injury prevention: Predicting fatigue and recovery timelines using movement data.
• Talent evaluation: Comparing college prospects with historical player archetypes.

Broadcasters and fans also benefit. Amazon’s Next Gen Stats and ESPN’s win probability models both rely on AI to calculate live odds and performance metrics — updating in real time as the game unfolds.

The technology has quietly become the game’s invisible analyst.

Can AI Really Predict the Outcome?

The question, then, is how far this can go. Can AI actually predict the winner of a football game?

The short answer: not perfectly — but impressively well.

AI models excel at probability-based prediction. Much like weather forecasts, they assign likelihoods to outcomes rather than absolute certainties. By combining hundreds of input features — from quarterback efficiency to team travel distance — these systems can produce win probabilities that often outperform human predictions.

For example:

• Teams with strong offensive line metrics might have a significant advantage in games played below freezing temperatures.
• Defenses using mixed coverage schemes may outperform blitz-heavy teams against mobile quarterbacks.
• Coaches with consistent fourth-down aggression statistically gain more long-term wins, a pattern AI models can quantify.

AI doesn’t claim omniscience — but it can reveal hidden tendencies that help explain results we previously thought were random.

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The Human Factor

Despite these advances, football remains an intensely human game. Motivation, leadership, and emotional momentum can swing outcomes in ways no algorithm can capture.

A locker room speech, a comeback drive, or a rookie’s unexpected breakthrough can shift the entire narrative. These intangible factors — chemistry, belief, passion — are beyond any dataset’s reach.

That’s why experts increasingly view AI as a companion tool rather than a replacement for human judgment. It strengthens analysis without erasing instinct. Coaches still call plays; AI simply makes the decisions more informed.

Where AI Already Excels

Even if full prediction remains elusive, AI is already excelling in specialized applications across the sport:

1. Game simulations – Running thousands of virtual versions of upcoming games to identify likely outcomes.
2. Injury forecasting – Using wearables and machine learning to estimate injury risk and recovery.
3. Recruiting and scouting – Evaluating college athletes with statistical comparisons to historical players.
4. Play-calling support – Offering coordinators data-driven suggestions for specific down-and-distance situations.
5. Fan engagement – Providing predictive dashboards and real-time insights during broadcasts.

AI has become the silent strategist — invisible on the field, yet deeply influential behind the scenes.

Lessons from Global Football (Soccer)

If you want proof that AI prediction works, you don’t need to look further than association football. Platforms like NerdyTips have analyzed over 170,000 global matches, leveraging algorithms to detect trends across leagues, continents, and playing styles.

By studying how AI successfully interprets soccer — a sport also defined by tactical variation, randomness, and emotion — we gain a roadmap for its potential in American football. The data may differ, but the principles remain: collect massive datasets, identify repeatable patterns, and continually retrain models as the game evolves.

What’s been achieved in soccer analytics can act as a blueprint for the next era of gridiron analysis.

The Future of Predictive Football

Looking ahead, the fusion of AI and American football is inevitable. In the next decade, predictive systems will grow even more sophisticated, integrating not just numerical data but also video recognition and behavioral analysis.

Imagine a system that can interpret a quarterback’s eye movement pre-snap, or recognize subtle shifts in offensive line spacing before a blitz. These micro-patterns — invisible to most viewers — could redefine how plays are analyzed and predicted.

Collegiate programs are already investing heavily in these tools, using them not only for scouting but also for in-game decision-making. As computing power increases and datasets expand, AI will gradually move from the analyst’s desk to the sideline tablet.

A Game Still Ruled by Uncertainty

So, can American football be predicted using AI? The honest answer is partially — and getting better every season.

Artificial intelligence won’t erase uncertainty, but it will continue to narrow it. It can’t predict the exact bounce of an oblong ball, but it can understand the thousands of decisions that make such moments possible.

In the end, football will always be defined by its unpredictability — that’s what makes it thrilling. But the next time a commentator calls a game-changing play “unexpected,” remember: somewhere, in a server full of historical data and machine learning code, an algorithm may have seen it coming.

We continue to chip away at our revised Notinhalloffame.com Hockey list for the 2026 vote and have updated 101-125.

The entire list (albeit under construction) is available here for your reference; the updated ranked players for the Hockey Hall of Fame consideration are:

*Denotes eligible for the first time.

101. Steve Duchene
102. Ed Jovanovski
103. Ken Hodge
104. Sid Smith
105. Terry Harper
106. Olaf Kolzig
107. John Ogrodnick
108. Saku Koivu
109. Pavol Denetria
110. Al Rollins
111. Dustin Brown
112. Jim Neilson
113. Ziggy Palffy
114. Gerry Galley
115. Kirk Muller
116. John Ross Roach
117. Ed Litzenberger
118. Bill White
119. Dan Boyle
120. Owen Nolan
121. Jack Crawford
122. Brad McCrimmon
123. Dany Heatley
124. Bobby Rousseau
125. Darryl Sydor 

Look for more updates soon.