Analyst’s Note: This report is a statistical analysis of a specific dataset comprising 1,000 horses whose odds shortened significantly between the Opening Live Market Odds and the Starting Price during the 2025 season. All findings, patterns, and conclusions are derived exclusively from this provided dataset and should be interpreted within this specific context.
For clarity, the following definitions are used throughout this document:
• Market Mover / Steamer: A horse included in this dataset, identified by a significant shortening of its odds.
• Win: A finishing position of ‘1’.
• Place: A finishing position of ‘2’ or ‘3’.
• Non-Finishers (UR, PU, F, CO) are treated as losses. One void race (VOI) has been excluded from all statistical calculations, resulting in a final sample size of 999 runners.
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1.0 Executive Summary
This report provides a detailed analysis of 999 significant market movers from the 2025 horse racing season. By examining the performance of these heavily backed horses, we aim to uncover statistical patterns that can help differentiate between genuine “smart money” and speculative “false steam.” The analysis dissects performance across several key variables—including trainer, jockey, race type, and the magnitude of the odds movement—to build a data-driven framework for betting strategy.
The most critical conclusions drawn from this dataset are:
• Race Type is the Premier Signal: The context of the race proved to be the most powerful performance indicator. Market movers in Handicap Chases were exceptionally potent, winning at a staggering 83.3% rate. In general, market intelligence in Jumps races (22.9% win rate) was far more reliable than in Flat races (12.8% win rate).
• Elite Trainers Convert Market Pressure: A clear cohort of trainers demonstrated a superior ability to ready a horse for a backed run. Daniel Skelton led this group, achieving a 60.0% win rate and 40.0% place rate from his five qualifying runners. Stables such as W J Haggas (44.4% win rate) and A M Balding (42.9% win rate) also showed that market support for their runners was well-founded.
• Jockey Skill on Backed Mounts is Evident: Certain jockeys excelled when the money was down. Cieren Fallon was the standout performer, converting 50.0% of his eight well-backed rides into wins. This suggests a strong correlation between his presence on a steamer and a genuine winning chance.
• Magnitude of Odds Move is a Secondary Factor: While dramatic odds collapses garner attention, the sheer size of the odds move was not a primary predictor of success on its own. The win rates for horses moving 4-10 points (14.4%) and those moving 10-25 points (14.6%) were nearly identical, indicating that the reason for the move (trainer, race type) is more important than the move itself.
Ultimately, this report serves as a practical tool for the modern handicapper. It moves beyond intuition, providing a statistical foundation to better interpret market signals and identify high-value betting and laying opportunities.
2.0 Key Findings
This section provides a condensed overview of the most statistically significant and actionable patterns identified within the 2025 market mover dataset. It is designed to serve as a quick reference for the core insights of the full analysis.
| At a Glance: Top Signals from the 2025 Market Mover Dataset | |
| Signal Type | Finding |
| Top Performing Trainer | Daniel Skelton: Achieved a 60.0% win rate and 40.0% place rate from 5 qualifying runners (3W, 2P). |
| Most Reliable Jockey | Cieren Fallon: Converted 50.0% of his 8 well-backed rides into wins (4W, 2P). |
| Strongest Race Type | Handicap Chase: Market movers in this category won 83.3% of the time (5 wins from 6 runners). |
| Optimal Move % Bracket | 91% and over: Horses in this bracket won 33.3% of the time, though from a small sample of 3 runners. |
| Key Lay Angle | Sam England: None of his 6 market movers won, and only two placed (0% win rate, 33.3% place rate). |
These high-level signals provide a snapshot of the key performance drivers within the dataset. The following sections will explore these patterns in greater detail, starting with an in-depth analysis of trainer performance.
3.0 Trainer Patterns: Identifying Stables That Attract Smart Money
The trainer is the single most important factor in a horse’s preparation, and their record with backed horses is a powerful indicator. Analyzing trainer data helps reveal which stables are proficient at readying a horse for a targeted win and, just as importantly, which are consistently overestimated by the market. This creates clear opportunities for both backing and laying horses based on the source of the market move.
The table below details the performance of all trainers with three or more market movers in the dataset.
| Trainer | Runners | Wins | Places | Win % | Place % | Avg. Finishing Position |
| Daniel Skelton | 5 | 3 | 2 | 60.0% | 40.0% | 1.60 |
| W J Haggas | 9 | 4 | 4 | 44.4% | 44.4% | 2.44 |
| A M Balding | 7 | 3 | 2 | 42.9% | 28.6% | 3.43 |
| Henry De Bromhead | 5 | 2 | 2 | 40.0% | 40.0% | 2.60 |
| Olly Murphy | 8 | 3 | 4 | 37.5% | 50.0% | 3.25 |
| N A Twiston-Davies | 8 | 3 | 2 | 37.5% | 25.0% | 2.50 |
| D K Weld | 3 | 1 | 2 | 33.3% | 66.7% | 2.00 |
| R D Potter | 3 | 1 | 1 | 33.3% | 33.3% | 2.67 |
| Donnacha Obrien | 3 | 1 | 0 | 33.3% | 0.0% | 4.00 |
| J P Murtagh | 6 | 2 | 0 | 33.3% | 0.0% | 5.67 |
| Joseph Patrick OBrien | 9 | 2 | 4 | 22.2% | 44.4% | 2.78 |
| Emmet Mullins | 5 | 1 | 1 | 20.0% | 20.0% | 5.00 |
| Archie Watson | 5 | 1 | 1 | 20.0% | 20.0% | 4.60 |
| J P Owen | 5 | 1 | 1 | 20.0% | 20.0% | 3.40 |
| C Byrnes | 11 | 2 | 1 | 18.2% | 9.1% | 5.60 |
| Richard Hannon (Jnr) | 9 | 1 | 1 | 11.1% | 11.1% | 7.56 |
| Gordon Elliott | 11 | 1 | 2 | 9.1% | 18.2% | 4.44 |
| Sam England | 6 | 0 | 2 | 0.0% | 33.3% | 4.67 |
| Charlie Johnston | 7 | 0 | 3 | 0.0% | 42.9% | 4.29 |
| L Russell & M Scudamore | 5 | 0 | 3 | 0.0% | 60.0% | 3.50 |
| R M Beckett | 6 | 0 | 5 | 0.0% | 83.3% | 3.17 |
| Ian Williams | 5 | 0 | 2 | 0.0% | 40.0% | 4.40 |
| James Ferguson | 3 | 0 | 1 | 0.0% | 33.3% | 6.67 |
| P D Evans | 4 | 0 | 1 | 0.0% | 25.0% | 6.50 |
| Micky Hammond | 3 | 0 | 0 | 0.0% | 0.0% | 5.33 |
The data clearly identifies a handful of trainers whose market movers commanded significant respect. Daniel Skelton leads this group with an outstanding record of 3 wins and 2 places from 5 runners (60.0% win rate). W J Haggas also boasts a formidable record, with 4 wins and 4 places from 9 runners (44.4% win rate), while A M Balding‘s runners converted market support into a win 42.9% of the time. These stables represent the strongest positive signals in the dataset.
Conversely, several trainers showed a poor correlation between market support and winning performance. Sam England (6 runners) and Charlie Johnston (7 runners) both failed to saddle a winner from their well-backed contingent. R M Beckett presents a nuanced case: while none of his 6 runners won, an impressive 5 of them placed, for an 83.3% place rate, suggesting a strong each-way or place-betting angle but a poor win-bet proposition.
A trainer’s ability to prepare a horse is only half the equation; the jockey’s execution in the race is equally vital. We now turn our analysis to the riders entrusted with these well-backed mounts.
4.0 Jockey Patterns: Gauging Rider Effectiveness on Backed Mounts
While the trainer prepares the horse, it is the jockey who must execute the race plan. This section evaluates which jockeys were most frequently aboard significant market movers and, more importantly, which riders proved most effective at converting that market confidence into wins and places. A reliable jockey on a steamer can be a powerful confirmation of the horse’s chances.
The table below analyzes the performance of all jockeys with three or more rides on market movers in this dataset.
| Jockey | Rides | Wins | Places | Win % | Place % | Avg. Finishing Position |
| Cieren Fallon | 8 | 4 | 2 | 50.0% | 25.0% | 2.13 |
| Oisin Murphy | 5 | 2 | 0 | 40.0% | 0.0% | 4.80 |
| Mr P Byrnes | 6 | 2 | 2 | 33.3% | 33.3% | 2.20 |
| Sam Twiston-Davies | 6 | 2 | 2 | 33.3% | 33.3% | 2.33 |
| Isabel Williams | 3 | 1 | 1 | 33.3% | 33.3% | 3.33 |
| David Egan | 7 | 2 | 2 | 28.6% | 28.6% | 3.57 |
| K T ONeill | 4 | 1 | 1 | 25.0% | 25.0% | 3.25 |
| W M Lordan | 4 | 1 | 0 | 25.0% | 0.0% | 3.50 |
| Tom Marquand | 5 | 1 | 2 | 20.0% | 40.0% | 3.00 |
| C T Keane | 6 | 1 | 2 | 16.7% | 33.3% | 4.00 |
| Daniel Tudhope | 6 | 1 | 1 | 16.7% | 16.7% | 5.17 |
| Mr Billy Loughnane | 8 | 1 | 3 | 12.5% | 37.5% | 3.50 |
| Ben M Coen | 10 | 1 | 0 | 10.0% | 0.0% | 7.10 |
| S M Levey | 6 | 0 | 3 | 0.0% | 50.0% | 4.00 |
| J W Kennedy | 5 | 0 | 2 | 0.0% | 40.0% | 4.00 |
| Jack Doughty | 5 | 0 | 2 | 0.0% | 40.0% | 5.80 |
The analysis highlights a clear tier of jockeys who delivered results when the money was down. Cieren Fallon was exceptionally effective, winning on 4 of his 8 heavily supported rides for a 50.0% strike rate. Oisin Murphy also performed well, converting 2 of his 5 steamer rides into wins (40.0% win rate). Mr P Byrnes was another reliable pilot, securing 2 wins and 2 places from 6 rides for a combined win/place rate of 66.7%.
In contrast, some jockeys showed a poor conversion rate on their backed mounts in this sample. S M Levey (6 rides), J W Kennedy (5 rides), and Jack Doughty (5 rides) all failed to register a win. Ben M Coen managed only 1 win from 10 opportunities (10.0% win rate) and posted a high average finishing position of 7.10, suggesting market confidence in his mounts was often misplaced.
Having examined the key human factors, we now shift our focus to the environmental context of the race itself.
5.0 Race Type & Class Analysis: Where the Money is Smartest
Not all market moves are created equal; the context of the race is a critical factor in determining the reliability of a gamble. A significant odds contraction in a small-field novice race implies something very different from a similar move in a competitive 20-runner handicap. This section dissects steamer performance by race type and code to identify where market confidence is most predictive of success and where it is most often misplaced.
| Market Mover Performance by Race Type | Runners | Wins | Win % | Place % |
| Handicap Chase | 6 | 5 | 83.3% | 0.0% |
| Hunters Chase | 5 | 2 | 40.0% | 40.0% |
| Beginners Chase | 11 | 3 | 27.3% | 27.3% |
| Maiden Hurdle | 45 | 11 | 24.4% | 22.2% |
| Handicap Hurdle | 40 | 6 | 15.0% | 20.0% |
| NH Flat | 45 | 6 | 13.3% | 24.4% |
| Handicap | 133 | 18 | 13.5% | 18.8% |
| Maiden | 87 | 11 | 12.6% | 18.4% |
The data reveals a distinct hierarchy in the reliability of market moves based on race type. The most powerful signal in the entire dataset came from Handicap Chases, where market movers won a remarkable 5 out of 6 races for an 83.3% win rate. Other Jumps contests like Hunters Chases (40.0% win rate) and Beginners Chases (27.3% win rate) also showed that market support was highly informative. In contrast, steamers in large-field Flat Handicaps (13.5% win rate) and Maidens (12.6% win rate) had a much lower success rate, suggesting market intelligence is less certain in these events.
To provide a broader perspective, we can group these races by code (Flat, Jumps, NH Flat).
| Market Mover Performance by Code | Runners | Wins | Win % | Place % |
| Jumps | 188 | 43 | 22.9% | 19.1% |
| NH Flat | 45 | 6 | 13.3% | 24.4% |
| Flat | 766 | 98 | 12.8% | 18.0% |
When aggregated, the analysis confirms that market moves on Jumps horses (22.9% win rate) were significantly more reliable than those on Flat runners (12.8% win rate). While the NH Flat category performed slightly better than the Flat overall, its win rate of 13.3% is still markedly lower than that seen in Jumps races, underscoring that market support over obstacles was the most dependable signal in this dataset.
This analysis shows that the type of race is crucial. Next, we examine if the magnitude of the odds move itself provides an equally strong signal.
6.0 Odds Movement Analysis: Quantifying the Impact of a Gamble
A central question for any handicapper is: how much of an odds move is truly significant? A horse dropping from 50/1 to 10/1 is visually dramatic, but is it a more potent signal than a horse contracting from 2/1 to Evens? This section analyzes performance by both the percentage of odds movement (Move%) and the absolute points dropped (Move) to identify the most predictive thresholds.
Analysis by Move Percentage (Move%)
This analysis segments the data by the percentage change between the opening odds and the starting price.
| Performance by Move % Bracket | Runners | Wins | Win % | Place % |
| 91% and over | 3 | 1 | 33.3% | 33.3% |
| 81-90% | 8 | 2 | 25.0% | 25.0% |
| 71-80% | 33 | 5 | 15.2% | 24.2% |
| 61-70% | 134 | 17 | 12.7% | 20.1% |
| 50-60% | 233 | 36 | 15.5% | 17.2% |
The results show that the most extreme percentage moves, while infrequent, yielded the highest returns. Horses in the 81-90% (25.0% win rate) and 91% and over (33.3% win rate) brackets performed exceptionally well, albeit from small sample sizes. The performance in the more populated lower brackets was clustered around the dataset average, indicating that a moderate percentage move, in isolation, is not a strong positive or negative signal.
Analysis by Points Movement (Move)
This table segments performance by the absolute number of points the odds have dropped, using the specified brackets.
| Performance by Points Movement Bracket | Runners | Wins | Win % | Place % |
| 4-10 pts | 605 | 87 | 14.4% | 18.5% |
| 10-25 pts | 391 | 57 | 14.6% | 18.4% |
| 25-50 pts | 3 | 0 | 0.0% | 100.0% |
The analysis by absolute points dropped reveals a surprisingly flat trend across the two largest brackets. Horses dropping 4-10 points won at a 14.4% rate, while those dropping 10-25 points won at a virtually identical 14.6% rate. This finding strongly suggests that the magnitude of the point move is a poor standalone indicator of success. The context of the move—such as the race type or trainer—is a far more critical factor than the raw number of points a horse’s odds have contracted.
Having analyzed these factors in isolation, the next section will synthesize these findings to create a profile of the most reliable market movers.
7.0 Profile of a Reliable Money Signal
This section synthesizes the preceding analyses to build a composite sketch of the “ideal” market mover based on this dataset. This profile represents the intersection of multiple factors that were most highly correlated with success, providing a blueprint for identifying what appears to be genuinely “smart money.”
Characteristics of ‘Smart Money’ Steamers
• Any Steamer in a Handicap Chase: This was the single most powerful angle in the dataset. Irrespective of other factors, market movers in this race type won 83.3% of the time (5 wins from 6 runners).
• A Daniel Skelton Jumps Raider: Horses trained by Daniel Skelton in Jumps races were formidable. All five of his qualifying runners were in Jumps races, producing 3 wins and 2 places for a 60.0% win rate and a 100% top-three finish rate.
• A Cieren Fallon-Ridden Steamer in a Flat Race: Our top-performing jockey excelled on the level. All 8 of his qualifying rides were on the Flat, where he delivered 4 wins for a 50.0% win rate. When the market supported a Fallon mount on the Flat, it was a highly potent signal.
• A Heavily Backed W J Haggas Runner: When a horse from the Haggas yard was subject to a significant percentage move (60% or more), they won 2 out of 3 times, for a 66.7% win rate.
8.0 Profile of an Unreliable Money Signal
Just as important as identifying positive signals is recognizing negative ones. This section profiles the characteristics of “false steam”—market movers that attracted significant support but consistently failed to deliver. These profiles can be used to identify strong laying opportunities or simply horses to avoid.
Characteristics of ‘False Steam’
• A Sam England or Charlie Johnston Steamer: These two trainers represented the strongest negative signals. They accounted for a combined 13 well-backed horses, none of which won (0% win rate). Market support for these stables was consistently and significantly misplaced.
• A Richard Hannon (Jnr) Runner with a Large Price Contraction: While the stable did train one winner, their overall performance was poor, particularly when the market move was substantial. The 7 Hannon runners whose odds moved by 9 or more points all lost, with an average finishing position of 8.7.
• A Jumps Steamer Ridden by J W Kennedy: Despite riding for top stables, jockey J W Kennedy failed to win on any of his 5 well-backed Jumps mounts. With 2 places and 2 non-finishers, his presence on a steamer over obstacles was a negative indicator in this sample.
• A Ben M Coen-Ridden Steamer in a Flat Maiden: This combination proved particularly unreliable. Coen had 5 rides on market movers in Flat Maidens, producing 0 wins, 0 places, and a high average finishing position of 8.2.
9.0 Actionable Betting Angles
This section translates the statistical analysis into a clear set of rules-based strategies for practical application. These angles represent the most compelling positive and negative trends uncovered in the 2025 dataset.
Top 5 Profitable Angles to Follow
1. Systematically back all market movers in Handicap Chase races. With an 83.3% win rate, this was the most profitable angle in the report.
2. Follow all Daniel Skelton-trained Jumps runners. This stable demonstrated a 60% win rate and 100% place rate with its backed horses over obstacles.
3. Back any horse ridden by Cieren Fallon on the Flat. His 50% strike rate on well-backed mounts makes him the go-to jockey in this dataset.
4. Support A M Balding runners in Flat races. The stable’s backed runners on the Flat won 3 out of 6 starts for a 50% win rate.
5. Consider each-way or place-only bets on R M Beckett steamers. This trainer had a 0% win rate but an exceptional 83.3% place rate, making his runners highly reliable to hit the frame.
Top 5 Lay/Avoid Angles
1. Systematically lay any horse trained by Sam England. A 0% win rate from 6 backed horses makes this a powerful negative signal.
2. Oppose any market mover from the Charlie Johnston stable. With 0 wins from 7 qualifying runners, this yard consistently failed to convert market support.
3. Avoid steamers ridden by Ben M Coen. A low 10.0% win rate from 10 rides and a high average finishing position of 7.10 marks him as a rider to be wary of on a backed horse.
4. Treat market moves in large-field Flat Handicaps with caution. The 13.5% win rate in this category suggests it is a prime area to look for value by opposing the steamer.
5. Lay runners from the Richard Hannon (Jnr) yard when they move more than 9 points. These runners were 0 for 7, showing a distinct pattern of underperformance when the gamble was significant.
These specific angles provide a clear framework for leveraging the patterns found in this dataset.
10.0 Final Recommendations
This report was designed to move beyond anecdotal evidence and use historical market data to build a more informed, analytical approach to betting on steamers. By dissecting the performance of 999 shortening horses, we have identified clear, statistically-backed patterns that distinguish reliable signals from market noise.
For the handicapper seeking to integrate these findings, the core strategy can be summarized by a few key principles:
• Always prioritize the context of the move over the magnitude alone. Race type proved to be the single greatest predictor of success. A moderate move in a Handicap Chase was infinitely more powerful than a huge move in a Flat Maiden.
• Trust proven Jumps intelligence. The data strongly indicates that market signals are far more reliable for National Hunt racing, especially in Chases and from elite trainers like Daniel Skelton.
• Identify specific trainer and jockey profiles. Not all support is equal. Backing steamers from high-performing stables (Skelton, Haggas) and those ridden by effective jockeys (Fallon) while avoiding those from underperforming sources provides a clear strategic advantage.
• Use the identified lay angles to find value. The ability to confidently oppose a weak, well-backed horse is one of the most powerful tools in a bettor’s arsenal. The negative profiles of certain trainers and race types provide a strong foundation for constructing lay bets or finding better value elsewhere in a race.
While no system is foolproof and past performance is not a guarantee of future results, this analysis demonstrates that an analytical approach based on tangible data provides a significant edge. By applying these principles, the discerning handicapper can better navigate the often-turbulent waters of market movers and make more informed betting decisions.
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