SportsLine publishes computer-driven projections that pair projected scores with a reported confidence percentage. These outputs are produced by a simulation-based projection model and should be read as probabilistic estimates, not guarantees. This guide explains how to read sportsline picks, convert odds into implied probabilities, compare model probabilities with market prices, and apply simple rules for sizing and risk control.

SportsLine computer picks come from thousands of simulated matchups and report a projected score plus a percent-hit metric.

Convert American odds to implied probability to compare market pricing with the model's stated confidence.

Look for gaps that remain after accounting for bookmaker margin and size decisions to your bankroll.

What SportsLine computer picks are and how to read them

SportsLine publishes computer-generated projections that combine projected scores, suggested sides or totals, and a stated confidence or hit rate. These computer picks come from a proprietary projection model that runs thousands of simulated matchups to produce distributions of outcomes, projected scores and percent-hit metrics CBS Sports model article.

Convert American odds to an implied probability quickly

Enter odds as whole numbers like 150 or -150

Think of a SportsLine pick as three linked pieces: the side or total the model favors, a projected score or combined points, and a reported percentage that represents how often the simulated runs showed that result. The percent figure is the share of model simulations where the outcome occurred and so can be read as the model's probability estimate rather than a promise of a win SportsLine model article.

SportsLine also states that its content is informational and not a guarantee of outcomes, so readers should treat projections accordingly and use responsible-play practices when deciding how to act on a pick SportsLine terms of use.

How the model works in practice: simulations, projections and confidence scores

The practical mechanism behind the published numbers is a simulation engine that runs a matchup many times to build a distribution of final scores. Each simulation applies the model's inputs and assumptions and records the outcome, and the aggregate of thousands of runs produces a projected score distribution that feeds the headline pick and confidence metric CBS Sports model article.

Because the detailed inputs and internal weights for the model are not publicly disclosed, the projections should be treated as black-box estimates. That means you can use the model's output but not assume you know every assumption behind it SportsLine model article.

One measurable output is the hit rate statement that appears in some writeups, for example a claim that a side hits in X percent of simulations. That X percent is simply the share of the simulation runs where that side covered or the total cleared the threshold, and it functions as the model's internal win probability for that specific bet type Implied Probability explanation.

How should I interpret SportsLine computer picks before acting on them?

Read the projected score and reported simulation percent as probabilistic estimates, convert sportsbook odds to implied probability for an apples-to-apples comparison, and act only when the model shows a meaningful gap that fits your risk rules.

Keep in mind that a reported 60 percent simulation hit rate can look attractive, but it still accepts that 40 percent of simulated outcomes did not favor that result. Simulations give you likelihoods, they do not change the underlying variance of a real game. When you read confidence numbers, treat them as probabilities to manage expectations and risk SportsLine terms of use.

Step-by-step: converting projections and odds into comparable probabilities

To compare a model confidence number with market pricing you first convert sportsbook odds into an implied probability. For American odds the translations are straightforward: for positive odds like +150 use formula implied = 100 / (odds + 100). For negative odds like -150 use implied = -odds / (-odds + 100). Investopedia provides this conversion method as a standard reference Implied Probability explanation.

Worked example, step 1, convert American odds to implied probability

Close up minimal score distribution chart with highlighted median and translucent confidence band on dark Funded Plays background for sportsline picks

Suppose a sportsbook lists Team A at +120 to cover a spread. For +120 the implied probability is 100 / (120 + 100) = 100 / 220 = 0.4545, or about 45.45 percent. Write that down as the market-implied chance of that outcome.

Worked example, step 2, read the model confidence

Imagine SportsLine publishes a pick saying Team A covers in 58 percent of simulations. That 58 percent is the model's estimated probability for the same outcome. To compare, convert both to the same units: you now have market-implied 45.45 percent and model-implied 58 percent.

View FundedPlays Challenges to practice structured prediction workflows

Try these conversion steps yourself on one pick. Convert the sportsbook odds into implied probability, note the model's reported simulation percentage, and compare the two numbers before deciding how to size a wager.

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Worked example, step 3, check the projected score against the spread

If the model offers a projected margin or final score you can check whether the projection implies a coverage of the posted spread. For example, a projected final that favors Team A by 5 points versus a market spread of Team A -3 means the model's median projection is 2 points better than the market margin, which may indicate an edge after you also check the implied probabilities.

Finally, remember conversions yield an apples-to-apples view, but they do not remove the natural variance of sports outcomes. A clear gap between probabilities helps justify action, but it does not guarantee a result Implied Probability explanation.

How to decide if a SportsLine pick shows an actionable edge

Measuring an actionable edge starts with comparing the model probability to the market probability after accounting for the bookmaker margin or vig. If the model's probability exceeds the market-implied probability by a consistent margin that survives the vig, you may have a theoretical edge to consider Point spread definition.

In principle a meaningful gap is one you could expect to realize over many similar decisions. That means you are looking for a repeatable difference where the model-implied probability is reliably higher than the market's price after fees, not a single data point that might be explained by noise CBS Sports model article.

Sizing decisions should be tied to personal thresholds and bankroll rules. Use modest, consistent stakes and reduce size when the gap is small. A simple rule is to risk a fixed fraction of your active bankroll and scale that fraction with the measured gap rather than chasing outsized returns on single picks Responsible play guide.

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Always verify any model projection against live consensus lines before acting. Markets change, sometimes quickly, and the model projection is an estimate made before or during market movement. If the market moves to remove your perceived edge, adjust or pass rather than forcing a trade Point spread definition.

Common mistakes when reading computer picks and how to avoid them

One frequent mistake is treating the model's percent as a guarantee. A stated 70 percent simulation hit rate means the model saw that outcome in 70 percent of simulated runs, not that the pick will win 70 percent of actual wagers. Always remember that simulations express likelihoods, not certainties SportsLine terms of use.

Another common error is failing to translate betting odds into implied probability before comparison. Comparing a raw percent from a model to an unconverted moneyline or spread number will give misleading impressions about edge if you do not convert the market price first Implied Probability explanation.

Finally, some readers rely on a model's past short-term success as proof of future performance. Models can have streaks and then suffer runs of bad outcomes. The safe corrective is to track a personal record against model picks over time and to stick to risk controls that limit drawdowns Responsible play guide.

Minimal 2D vector side by side comparison of implied probability conversion from American odds and model simulation percentage in Funded Plays palette for sportsline picks

Practical examples and scenarios: working through three concrete reads

Example A, clear margin advantage against the spread

Model projection: SportsLine projects Team B to win by 8 points and reports Team B covers the spread in 72 percent of simulations. Market: the consensus spread shows Team B -3 and the implied market probability for covering the spread is 58 percent after converting the listed odds. In that case the model and market differ meaningfully and the model shows a sizable advantage in both margin and probability SportsLine model article.

Takeaway: a consistent difference of this size may be actionable, but only after checking the live lines and sizing the decision to your bankroll rules.

Example B, spread close but total differs

Model projection: a projected combined score suggests the total will be 220 whereas the market over/under sits at 215. The model's simulations show the total clearing the line in 62 percent of runs, while market-implied probability is 53 percent. The spread may be close, yet the total shows a clearer gap, which can make the total the preferable bet in this scenario CBS Sports model article.

Takeaway: use the projection that shows the cleanest, convertible advantage between model probability and market-implied probability.

Example C, small model edge that fails to cover vig

Model projection: SportsLine reports a 52 percent simulation hit rate on a side. Market: after converting odds and including a standard bookmaker margin, the market-implied probability is 50 percent. The raw gap is small and likely disappears after considering vig and transaction costs. In this case, the model edge is not large enough to justify action at normal stake sizes Implied Probability explanation.

Takeaway: small differences can be noise. Prefer choices where the model advantage remains after you account for fees and sizing.

Summary and next steps for readers who want to use model picks responsibly

Checklist before acting on a pick: convert the sportsbook odds to implied probability, compare that probability with the model's stated simulation percent, check the projected score against the live spread or total, size stakes according to bankroll rules, and follow responsible-play guidance Responsible play guide.

Keep records and track results personally. Even a well-specified model can produce strings of bad outcomes. Use the model as a decision aid, not a guarantee, and adhere to the platform terms that make clear content is informational and not a promise of returns SportsLine terms of use.

What does a SportsLine confidence percentage mean?

A confidence percentage shows the share of model simulations in which the outcome occurred; it is a model-based probability estimate, not a guarantee.

How do I convert American odds to implied probability?

For positive odds use 100 divided by (odds plus 100). For negative odds use the absolute odds divided by that absolute plus 100. This gives the market-implied probability.

Should I always follow a model pick with money?

No. Use the pick as input, convert odds, check live lines, size to your bankroll, and follow responsible-play rules before acting.

Model projections can be a useful part of a disciplined approach if you treat them as informed estimates rather than certainties. Convert odds, compare probabilities, verify live lines, and keep strict bankroll rules. If you want to test a model-informed workflow, log results over time and adjust thresholds based on your own tracked performance.