This article is a practical guide for sports fans and prediction users who want to turn live 3m open odds into a timestamped, evidence-backed list of favorites. It walks through the math to convert prices into implied probabilities, shows how to align market snapshots with DataGolf probabilities, and explains how TPC Twin Cities course traits affect who tends to be favored. The focus is on a repeatable workflow you can use during tournament week. By recording exact retrieval times for both market prices and model outputs you make your decisions auditable and easier to refine over time.

Convert bookmaker prices into implied probabilities to compare markets and models on a consistent basis.

Match timestamps between your odds snapshot and DataGolf probabilities to avoid timing mismatches.

Use repeated snapshots and simple edge calculations to flag potential value over the course of tournament week.

Quick snapshot: what '3m open odds' are telling you right now

Bookmaker winner prices are the market shorthand for who bettors think can win, but those prices move with money and news, so a single snapshot needs a timestamp to be useful for comparison or record keeping. Odds aggregator pages collect current prices across sportsbooks and provide a convenient snapshot of the market at a given time, which is why working from a timestamped capture is essential for reproducing analysis OddsChecker winner market

Who are the favorites to win the 3M Open?

Favorites are best defined by a timestamped comparison of market-implied probabilities and independent model probabilities; convert live 3m open odds into implied probabilities, align them with DataGolf probabilities, and rank players by a reproducible edge calculation.

When you see a headline like 3m open odds favoring a player, treat it as a starting point, not a conclusion. The raw price tells you what the market is currently paying, and the price can be converted into an implied probability to make apples to apples comparisons with model outputs and histories.

What this article covers

This article lays out the conversion from price to implied probability, explains how to align a market snapshot with a DataGolf event probability page, and gives a stepwise workflow you can use during tournament week to produce a timestamped favorites ranking. It also explains how TPC Twin Cities course traits affect which players often show up near the top of the market.

How to read a quick market snapshot

Start by recording the aggregator page name, the list of prices you see, and an exact retrieval time. That timestamp is the anchor that lets you compare the market to a model snapshot taken at or near the same moment; without matching timestamps you risk comparing different market conditions and drawing the wrong conclusions OddsChecker winner market

Why convert odds to implied probabilities before ranking favorites

Odds are convenient for betting but inconsistent across formats; converting them to implied probability creates a consistent numeric basis for comparison and aggregation. The basic formula for decimal odds is implied probability = 1 / decimal odds, and you can use that same idea for fractional or American formats once you convert to decimals first Investopedia implied probability

Implied probability: the formula and a short example

Top down close photo of a golf scorecard beside a laptop showing a spreadsheet and 3m open odds on a minimalist dark desk

For example, decimal odds of 10.0 convert to an implied probability of 1 / 10.0 = 0.10, or 10 percent. If a market shows two different books with slightly different decimal prices, converting each to implied probability lets you average or select the sharpest price before removing the bookmaker overround.

Why probabilities make model comparisons meaningful

Model outputs such as win probabilities are already in percentage form, which is why converting market prices into implied probabilities is the natural step for comparison. For precise work you should also account for the vig or overround from the market snapshot, because the raw implied probabilities will sum to more than 100 percent when bookmakers add margin Investopedia implied probability

How to compare bookmaker-implied chances with DataGolf model probabilities

DataGolf publishes model-based probabilities for wins and top finishes and updates those figures during the tournament week; matching a market snapshot with the DataGolf event page taken at comparable times is the core of any useful market versus model comparison DataGolf methodology

quick per-player edge calculation to compare market implied probability with model probability

enter decimal odds then paste model probability

To align formats, convert every bookmaker price you captured into decimal odds and then into implied probability. Put the DataGolf win probability next to that implied number and compute edge = model probability minus market implied probability. Positive edge suggests model sees more chance than the market is pricing, negative edge suggests the market is pricing a greater chance than the model

Where to get DataGolf event probabilities

Check the DataGolf event page for the 3M Open during tournament week to capture the model's published win and top-5 probabilities; keep the DataGolf page timestamp as part of your record so you can show you compared contemporaneous snapshots rather than stale outputs DataGolf methodology or the DataGolf homepage

Aligning market and model formats

Use a single worksheet or tool to store three columns: decimal odds, market implied probability, and model probability. Make sure both market and model columns reference the timestamp you recorded. That alignment eliminates a common source of error where changes in lines or updates to the model cause mismatches in a later comparison.

Simple example calculation and interpretation

Imagine the market has a player at decimal 20.0, so implied probability is 5 percent, while DataGolf shows a 7 percent win probability. The simple subtraction gives a model minus market edge of 2 percentage points. That gap is the basis for flagging a potential value pick, while remembering to check for news-driven reasons the market might be assigning a different probability DataGolf methodology

Course and field context: how TPC Twin Cities shapes who the favorites should be

The 3M Open is played at TPC Twin Cities in Blaine, Minnesota and is typically scheduled in late July, so seasonal form and travel schedules around that timing influence which players enter as favorites PGA TOUR schedule

Key course traits that affect winning chances

TPC Twin Cities is a par-71 layout that has tended to play on the easier side by scoring relative to par in recent seasons, which means lower winning totals and a profile that favors players who can consistently make birdies and recover from mid-range misses TPC Twin Cities course overview

Minimalist 2D vector worksheet with four columns for decimal odds implied probability DataGolf probability and edge highlighted accents 3m open odds

Because the course often yields plenty of birdie chances, market favorites at this stop frequently include players who are good putters from short range and who excel at scrambling when tee-to-green shots aren’t perfect. That course tendency is context for assessing which market favorites make sense and which may be overvalued.

How season-level scoring and difficulty fit into the picture

Seasonal difficulty rankings provide helpful context but should not be treated as determinative; DataGolf's course difficulty measures and season scoring trends explain where the course sits relative to other events, but combine those rankings with current form and model probabilities rather than using them alone to pick favorites DataGolf course difficulty

Practical step-by-step: build a timestamped favorites list from market odds and model output

Step 1, capture a timestamped snapshot from an odds aggregator and save the list of winner prices with the exact time you retrieved them. Keeping that exact snapshot lets you audit any later decisions and ensures your comparison to a model snapshot is reproducible OddsChecker winner market

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Step 2, open the DataGolf event page for the 3M Open and note the model's published win probabilities and the time the model values were posted. Include both timestamps in your worksheet so you can show the market and model were aligned when you compared them DataGolf methodology

Step 3: convert and compare

Step 3, convert each decimal price to implied probability using 1 / decimal odds, then subtract the market implied probability from the DataGolf model probability to produce an edge column. Flag players with consistent positive edges across multiple snapshots as potential value candidates and document any news items that could explain short-term market shifts Investopedia implied probability

Format for a simple favorites list

Rank players by model probability first, then annotate each with market implied probability and the computed edge. Use value tags such as "market < model" to indicate where the model assigns more win chance than the market. Keep a notes column for injury, travel, or weather items that may justify market deviations. (see the Funded Plays blog)

Common mistakes and pitfalls when reading 3m open odds

Ignoring ties and field size distorts implied probabilities. In large fields tie adjustments and the number of starters change how the market divides probability across players, so make sure your implied probability conversion and subsequent aggregation respect the field context rather than assuming the same baseline for every event OddsChecker winner market

Using stale odds or mismatched timestamps creates a false signal. If your DataGolf model snapshot is from a different time than your market snapshot you may see edges that reflect timing differences rather than genuine disagreement between market and model DataGolf methodology

Overweighting course difficulty or one-off stats

Course difficulty numbers are useful context, but treating them as deterministic can lead you to overweight a single metric. Use course scoring trends as one input among many and combine them with recent player form to avoid common analytic mistakes DataGolf course difficulty

Worked example: annotated market vs model comparison (illustrative)

Below is an illustrative snapshot to show the mechanics. This is a non-live, hypothetical example meant to mirror the step-by-step workflow and clarify interpretation choices. Start by noting your market snapshot time and the DataGolf page time before you run the numbers. For additional context, see golf.com's betting guide.

Example table, described: Player A market decimal 12.0 implied 8.33 percent, DataGolf model 6.5 percent, edge -1.83 percent; Player B market decimal 50.0 implied 2.0 percent, DataGolf model 3.5 percent, edge +1.5 percent; Player C market decimal 20.0 implied 5.0 percent, DataGolf model 4.8 percent, edge -0.2 percent. These annotated discrepancies show common types of gaps you will encounter and how to label them in your favorites list DataGolf methodology

Try the FundedPlays Challenges workflow for disciplined prediction practice

Capture a timestamped odds snapshot now and run the simple calculation using the worksheet approach to see if any players show a consistent model edge worth tracking

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How to interpret each discrepancy: a market favorite with negative edge may be overbet relative to the model, but first check for injury news, late tee time differences, or other market-moving items; a market outsider with positive edge is a potential value candidate, but track that edge over multiple snapshots before acting.

Suggested conservative actions include logging edges across several snapshots, limiting exposure to single-event wagers, and using risk sizing rules so one event does not dominate your record. Treat model and market as complementary inputs rather than absolute answers.

Decision criteria: when to trust the market, when to lean on the model

Prefer the market when you observe sharp movement into a player concurrent with verifiable news, such as an injury update or a confirmed withdrawal that changes field composition; those moves reflect information that the model may not yet incorporate OddsChecker winner market and can be triangulated with reporting such as CBS Sports odds coverage.

Checklist for preferring market prices

Follow the market when there is clear, verifiable news that explains a price move, when you see consistent liquidity across large books, or when the market displays a coherent shift supported by multiple sources. Record the timestamp and the reason in your log. (see how Funded Plays evaluations work)

Checklist for preferring model signals

Lean on the model when you observe stable positive edges across repeated snapshots, when there is no clear news to justify a market move, and when model inputs such as recent form and hole-by-hole performance favor a player despite a longer market price DataGolf methodology

Basic risk management suggestions

Use small, consistent sizing rules and track each pick's performance against both market and model expectations. Avoid overconcentration on a single tournament and treat your favorites list as a living document you update with new snapshots and model refreshes Investopedia implied probability

Conclusion and quick checklist for producing a defensible favorites list

Synthesize your findings by ensuring each favorites ranking is backed by a timestamped market snapshot, a contemporaneous DataGolf probability, and a simple edge calculation that you can reproduce. The discipline of recording timestamps and annotating reasons for market divergence is what makes a favorites list defensible rather than anecdotal DataGolf methodology (see Funded Plays)

Printable checklist for tournament week

1) Capture odds from an aggregator and record the retrieval time. 2) Pull DataGolf event probabilities and note the model timestamp. 3) Convert odds to implied probabilities and compute edge. 4) Flag repeated positive edges and cross-check for news. Repeat through tournament week and keep a concise log OddsChecker winner market

How do I convert odds into implied probability?

Convert decimal odds using implied probability = 1 / decimal odds. Convert fractional or American to decimals first, then apply the formula and adjust for bookmaker vig if needed.

Where do I find model probabilities for the 3M Open?

DataGolf publishes event-level model probabilities, including win and top-5 chances, and updates those during tournament week.

Should I trust a single snapshot of market odds?

No. Use timestamped snapshots and track changes across multiple captures to avoid reacting to short-term noise or isolated market moves.

Use the checklist in this article as your baseline for every tournament week. Consistent timestamping, careful conversion of odds into probabilities, and tracking edges across multiple snapshots are simple practices that improve decision making over time. Treat both market prices and model outputs as tools. Neither guarantees outcomes, but combined they give you a clearer view of where potential value may lie.