In golf how often has betting the field won

It's the hardest market to win given that average field sizes can include players, but as the golf odds are so high, is the most rewarding. It's important to stay reasonable and in golf how often has betting the field won to a weekly limit on how much you bet on a tournament. Picking an outright winner is hard and you will often go. When it comes to golf, hitting a winner ( Narrowing down a field of players to 25 who have a chance to win However, he accidentally. Long shot bets for this week's Wells Fargo from golf betting expert Steve Rawlings who has picked out five outsiders on the Betfair exchange.

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On the true strokes-gained page , why don't the strokes-gained categories add up to strokes-gained total in the yearly summary tables. Only events that have the ShotLink system set up provide data on player performance in the strokes-gained categories. Therefore, the true strokes-gained numbers in each category are derived from this subset of events, while the true strokes-gained total numbers are derived from all events in our data PGA Tour, European Tour, Web.

If every tournament a golfer played in a given season had the ShotLink system in place, then the sum of the true SG categories will equal true SG total. On the true strokes-gained page , why do you have to impute some of the strokes-gained category values?

Imputation is only necessary for some — but really most — European Tour events. The Euro Tour started tracking strokes-gained category data in late On their website they only make available event-level strokes-gained averages rather than the raw round-level data unless you pull the data immediately after each round is played, which has its problems as there are often data errors.

For the few European Tour events where we have successfully collected the round-level data for each SG category, we obviously just display that. It's also worth noting here that the SG category data from the Euro Tour is typically missing for a few players in each event. In the other events where only event averages are available, we have to get creative.

For the purposes of incorporating this data into our predictive model this is not a big issue; ideally we would like to know the values for individual rounds as more weight is applied to more recent rounds, but using the same value for all rounds played within an event only changes things slightly.

However, given the information we have — event-level strokes-gained category averages and total strokes-gained for each round — we can actually do a bit better than just using event averages. We fit a regression model using PGA Tour data where we actually have round-level strokes-gained to estimate the relationship between the relevant variables i. We can then use that model to predict i.

A few notes on these imputed values: they will add up to the actual event-level averages in each category; they will show less variation than the true unobserved to us round-level data; and the imputed values for putting and approach will vary more than off-the-tee and around-the-green. That is, if a golfer gained in total 5 strokes more in round 2 than round 1, more of that difference will be attributed to strokes-gained approach and putting than to off-the-tee and around-the-green.

These imputed values will only make a difference in the true SG query tool if you select a sample e. Why is true strokes-gained not exactly equal to raw strokes-gained plus your estimates of the field's average player quality. Hello, interested reader. Welcome to the weeds. In a perfect world, true strokes-gained as it appears on our website would be equal to raw strokes-gained i.

However, this is not quite true for two reasons. The first reason is fairly innocuous: our field strength page shows the average skill level for the players in round 1 of a tournament. In golf how often has betting the field won Therefore, for rounds played after a cut is imposed on the field, the average skill level will differ slightly from that listed. The second reason is more technical, and accounts for why even round 1 true SG values will differ from raw SG plus the field's listed strength.

The issue is that to estimate true strokes-gained, we require estimates of players' skill; but, to estimate a player's skill, we require true strokes-gained. In theory, we could perform our entire estimation procedure in a big loop, and stop once our estimates of player skill converge from one iteration to the next, but this would be very computationally expensive and result in marginal gains.

Therefore, the problem is this: the measures of field strength used when estimating true strokes-gained are ultimately not the same as those that appear on the field strength page. The details of the strokes-gained adjustment are here. One good reason to keep things as they are now, is that the field strength measures estimated in the score adjustment method use data from both before and after a tournament.

That is, when we retroactively estimate true strokes-gained values for the Travelers Championship as we do every week , the fact that Scottie Scheffler played very well after that tournament increases the field strength and hence the true SG values compared to what our estimate was the week immediately following the Travelers.

In contrast, field strength as estimated in our predictive model only uses data from before an event is played, as, naturally, that is all we have when making predictions. And these are the values that are displayed on the field strength page. In general, these two measures of field strength should be very similar within 0. What are adjusted driving distance and adjusted driving accuracy?

Adjusted driving distance which only uses the two officially-measured drives for each round is the number of yards gained over the field's average drive, adjusted for the driving distance strength of that field. Adjusted driving accuracy is the percentage of fairways hit gained over the field's average, again adjusted for the driving accuracy strength of the field.

Some examples will be illustrative. First, for every golfer in a given field we have an estimate of their expected driving distance and expected driving accuracy. That is, an estimate of how far we expect them to hit their next drive, and an estimate of the percentage of fairways we expect them to hit.

We express these relative to an average PGA Tour player: e. For clarity's sake, let's call these estimates "distance skill" and "accuracy skill". Now, suppose a golfer hits their 2 measured drives in a round an average of yards while the field averages yards. The adjusted driving accuracy value for this golfer would be 5.

Note that we are talking about percentage points here, not percent differences. We could equally describe driving accuracy in terms of fairways hit i. How do I interpret player skill profiles and accompanying radar plots. Our skill profiles, displayed as radar plots, show the number of standard deviations better or worse a player is in each skill relative to the PGA Tour average.

Standard deviation is a measure of the spread of the data; for our purposes, here are the relevant standard deviations: driving distance 8. Therefore, if you are 1 standard deviation above average in driving distance, this means you are 8. Nearly all of the data will be within 3 standard deviations of the mean i. For more intuition on standard deviation, take a look at this Wikipedia entry.

The skill ratings page displays skill estimates in their raw units; we simply divide these values by their respective standard deviations listed above before displaying them in the radar plots. In theory adding LIV data to our model is no different than any adding other tour. This gives us sufficient overlap for reasonable ongoing estimates of LIV players' skill relative to the rest of professional golf.

If LIV were ever to become a true "closed shop", with LIV golfers only playing in LIV events, after a while there wouldn't be any way of knowing what the relative skill of their fields are. Even in the current situation, we do have some concerns about how accurately we are estimating LIV field strengths. The potential problem here is that the events we are using to compare LIV and non-LIV golfers tend to be events where we would expect LIV golfers to underperform, which would make our estimates of LIV players' skill lower, all else equal.

This effect would likely be pretty small 0. There is no shot-level data at LIV events, but this doesn't present that much of a problem. Total SG adjusted for field strength is the only input into our rankings and is also the most important input into our predictive model.

It's unfortunate that we won't have high-quality data on LIV golfers' performance in the SG categories from a fan perspective, but it's not a huge loss for our model. Has the true strokes-gained baseline changed now that some of the game's top players have moved to LIV?

In the past, despite ambiguous language on parts of our site, the average true strokes-gained value for all Shotlink-enabled PGA Tour rounds in a given year was set to zero. We restricted to Shotlink rounds in an attempt to define a consistent group of events across years.

That is, True SG told us how much better a given performance was than what we would expect from an average PGA Tour field in that year. Even before the introduction of LIV in , using PGA Tour fields as our baseline had some problems; in seasons where the PGA Tour was relatively weak due to Europe's top players performing well, for example , our baseline would also be relatively weak.

This obviously doesn't matter for within-season comparisons, but when looking across seasons it would make performances in weak-baseline years seem slightly better than they actually were. The solution to this problem is to make the baseline tour-independent. We now use the average performance of players ranked between th in a given season as the True SG baseline.

It was surprisingly difficult to define a consistent baseline across all years because our data coverage has improved over time e. However, it's interesting that the magnitude of this decline in PGA Tour field strength is fairly small, and actually not so different from some past years e. Expected wins measure the likelihood of a given strokes-gained performance resulting in a win.

Why would this be good enough to win some events, but not others. Sometimes another player may also happen to have a great week and gain more than 3 strokes per round, while other weeks this doesn't happen. The intuition behind the expected wins calculation is simple. In practice, it's not quite this simple as the number of strokes-gained performances exactly equal to 3 will be small.

Therefore some smoothing must be performed — see graphs below. Expected wins based on raw strokes-gained as a statistic carries with it the same drawbacks of raw strokes-gained: it can't be compared across tournaments of differing field strengths. Pga golf predictions today Further, tournament characteristics like field size also confound raw SG-based expected wins comparisons all else equal, the larger the field, the larger the raw SG typically required to win.

For these reasons our website dispays what could be called "true" expected wins. In words, this measures the likelihood of a given 4-round performance winning some baseline event e. Like true strokes-gained , true expected wins from any tournament or tour can be compared. To get a sense of the relationship between true strokes-gained and winning on the PGA Tour, shown below is a histogram of the 4-round true SG average of every winner from at what I've dubbed "average" PGA Tour events i.

The simple answer is that it is because our model is not perfect. That is, the bookmaker's odds contain information that is not reflected in our model's probabilities that is useful for predicting performance. This is not suprising. The only way our actual profit would match our expected profit in the long-run is if our model's estimates could not be improved upon by incorporating the bookmaker's odds.

One way to get around this is to include the bookmaker's odds in your modelling process. This would make actual profit line up with expected profit under a few assumptions. We talk about related ideas in an old betting blog. The fact that our model's expected profit overestimates 'true' expected profit is why we use a threshold rule to determine when to place a bet. For more details on the relationship between our model's EV and actual returns, see the final section of this blog.

All bets are placed through Bet, so the first criteria is that the bet is offered there. For each bet type matchups, 3-balls, Top 20s, etc. The specific value of these thresholds have tended to evolve over time. The longer are the odds, the higher the threshold is. We also do not place 3-ball or matchup bets if we have very little data on any of the players involved cutoff is around 50 rounds.

We do this because our predictions for low-data players have much more uncertainty around them. Bets are typically displayed on the page as soon as play begins on a given day sometimes a half-hour to an hour after play begins. For Scratch members bets can be viewed as soon as we make them ourselves typically well before play begins.

We use a scaled-down version of the Kelly Criterion. The Kelly staking strategy tells you how much of your bankroll to wager, and is an increasing function of your percieved edge i. Importantly, the Kelly is designed for sequential bets; i. However, in golf betting we will often have many simultaneously active bets.

We don't have a fully worked solution to this, but sometimes we will lower the Kelly fraction if there are already a lot of units in play. In golf how often has betting the field won This is one reason you won't be able to find a consistent Kelly fraction when analyzing our wagers; the second reason is that we vary the Kelly fraction by bet type and have also varied it over time as our poorly-formed betting strategy has evolved.

How we arrive at our pre-tournament estimates of player skill and our pre-tournament finish probabilities is described in detail here. Once the tournament is underway, the largest updates to a golfer's finish probabilities are due to their performance so far in the tournament e. However, we also use the live scoring data to update our pre-tournament predictions of golfer skill, and to update our estimates of each hole's difficulty.

Regarding the latter, we also predict how each hole will play in the morning and afternoon when there are distinct waves of each remaining round. As with our pre-tournament simulations, the effects of pressure are accounted for in the live model. This has an effect on a player's projected skill for the 3rd and 4th rounds.

Therefore, for the Matchup Tool, Props Tool, and Custom Simulation which all use the live model simulations , our probabilities account for any changes to golfers' predicted skill due to their performance so far in the tournament and their position on the leaderboard i.

For interested readers, here are a few more details. With respect to predicting hole difficulty, it is critical to correctly account for the uncertainty in difficulty. For example, on Thursday evening, not only is it important to be able to accurately estimate the expected difference in scoring conditions between Friday morning and afternoon, but also to accurately characterize the range of possible conditions.

If the afternoon wave is expected to face a course that is 0. Back in the earlier days of the live model we didn't properly account for this uncertainty, and took a lot of grief for it when projecting the cutline. With respect to updating golfers' skill levels, we have fit models that inform us on how much to update a golfer's Round 2 skill level based on their pre-tournament data and their Round 1 performance and analogous models for Rounds 3 and 4.

When available, we incorporate the strokes-gained categories into these updates. All of this information is finally put to use in the simulations that ultimately generate the coveted finish probabilities. Each simulation starts by drawing random numbers to determine the course conditions, e. Thursday's morning wave faces a course that is 0.

This process repeats itself until the 4th round is simulated, at which point each golfer's finish position can be determined; perform many simulations like this and you end up with probabilities e. Hopefully this illustrates that our live model is internally consistent ; every update that we will eventually make once the real data comes in e.

Why do the Top 5 and Top 20 probabilities add up to more than they "should" i. This the case because the live model is simulated with ties allowed. As a consequence, the default Top 5 and Top 20 probabilities provided are not suitable for making in-play bets where ties are resolved by dead-heat rules. They will indicate more value than they should because they do not take into account the reduced payouts received when there are golfers tied for the final paid finish positions.

What does it mean for a Data Golf probability to 'account for dead-heat rules'. First, if needed, read this for a primer on what dead-heat rules are. Second, recall that an 'implied probability' from a bookmaker is the probability required for the bet to have an expected value of zero. Once you have this implied probability, a simple comparison with our predicted win probability is all that is required to assess the expected value of the bet.

By way of example and for simplicity, suppose there is a top 5 bet where the only possible outcomes are for a golfer to finish in the top 5 golfers with no players tied for 5th , to finish tied for 5th with 1 other golfer, and to finish outside the top 5 golfers. The first term in brackets here is what we are calling a 'probability that accounts for dead-heat rules'.

This is intuitive: in cases where the golfer's finish position results in the application of dead-heat rules, we multiply the probability of that outcome occurring by the dead-heat fraction that gets applied to the payout e. These match values are meant to capture how influential a given match is on the outcome of the tournament.

Consider a match between Golfers A and B. For every golfer in the field, we estimate two win probabilities: their win probability if A wins the match, and their win probability if B wins. We then take the magnitude of the difference between these win probabilities for each golfer, sum them up, and we have our match value.

In the first round of the Match Play tournament, the match values are mainly driven by their effects on the win probabilities of the golfers involved in the match. For example, in the edition of this event, Jon Rahm was the favourite at 6. A win in his first round match against Sebastian Munoz would give him a win probability of 8. The difference for Munoz between a Rahm win and a Rahm loss was To arrive at our final match value of 0.

In the second and third rounds of the group stage the match values can get more interesting. Here there can be large effects on the win probabilities of golfers not involved directly in the match of interest. These affected golfers will most likely be in the same group as those involved in the match, but there can also be scenarios where large effects are seen on golfers outside the group.

For example, suppose in the third round Jon Rahm is playing a lower-ranked player and Rahm must win the match to advance. As the best player in the field, if Rahm is eliminated this substantially increases the win probability of the field compared to the scenario where he wins.

Hopefully these match values can provide some interesting insight as the tournament progresses. We've tried to follow the PGA Tour's methods for calculating strokes-gained as closely as possible, but inevitably there will be differences given we don't know exactly what their process is.

Here are a few of the common sources of disagreement: 1 labelling "recovery" shots. The expected strokes to hole out changes substantially if a shot is deemed to be hit from a recovery position see p. Labelling a shot as a recovery will have the effect of decreasing the previous shot's SG and increasing the SG of the current shot. If a player doesn't hit a shot in every category on every hole, these two adjustment methods will yield different SG estimates.

SG:ARG is the category most commonly affected by this; 4 We make the adjustment mentioned in 3 regardless of how many players have finished a hole read more below , whereas the PGA Tour only starts making their adjustment later in the round. This will contribute to discrepancies for all categories while a round is ongoing.

How is it possible for players to have non-zero SG values on holes where they didn't hit a shot in that category. To arrive at the final SG category values displayed on the scorecard, we subtract the average baseline strokes-gained value in each category. This adjustment occurs regardless of how many players have played the hole.

For example, if only 2 players have played a hole and they both made foot putts, their strokes-gained putting is zero on that hole. This adjustment ensures the strokes-gained categories always add up to total strokes-gained. To see why this happens, consider a hole that is of average difficulty on approach shots but is harder than average around the green i.

Intuitively, this player gained strokes around the green by not having to play from a location e. An alternative method would be to tailor a baseline strokes-gained function to each hole. That is, to have hole-specific estimates of how many strokes it takes an average pro to hole out from each location. In our example above, the player would have all their SG allocated to the approach category.

This comes from the fact that the expected strokes to hole out from yards would be higher because missing the green comes with a bigger penalty , and so a shot hit to 20 feet might gain 0. This is a drawback in the sense we get very little information regarding the true quality of the players' approach shots on this hole. In any case, the more important consideration here is that the second method is very hard to implement, especially live during the tournament.

It's mainly for that reason that we stick to the simple adjustment of subtracting off the mean SG by category and hole. A golfer's projection is the expected number of points we are predicting they will earn. We form these projections by using the output from our predictive model to simulate each golfer's performance at the hole level.

A hole-level simulation is necessary to simulate fantasy scoring points , which depend on hole-specific scores as well as a golfer's performance on consecutive holes. By performing many simulations we can obtain a distribution for each golfer's earned fantasy points; the projection is then simply the average point value across all simulations.

As said above, our fantasy projections are generated using the predicted skill levels from our predictive model. Therefore, it is not actually the case that our default fantasy projections are a weighted average of long-term form and short-term form. When you move the long-term weight, we compare the golfer's long-term last 2 years form to their short-term last 3 months form, and adjust the projection accordingly depending on whether it is higher or lower, and whether you've increased or decreased the weight.

The same applies for short-term form. The weighting adjustment has to be done this way to accomodate the fact that we want our optimal projection to use a continous weighting scheme, while also giving users the ability to make their own simple adjustments to long-term form versus short-term form.

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Narrowing down a field of players to 25 who have a chance to win can be difficult enough, but to pinpoint who is going to have their best stuff on a given week is nearly impossible. Some of the biggest golf bets are those that include more sports than just golf.

Gerry McIlroy is one of those fathers. One time he made a golf bet with friends that just kept escalating. However, he accidentally placed the bet on Paul Lawrie who actually won, as did Kyle Stanley. Reason 38,, why Arnold Palmer is the best. On Tuesday, prominent sports betting outlet Bovada posted this unbelievable winning ticket of a two-way golf parlay which one of their users made prior to the Honda Classic, where Rickie Fowler won back in February.