SA 101 MLB: what the stat is and why it shows up in baseball games
A line drive pulled to right field, a base hit on a hanging curveball, a hard groundout straight to the shortstop. In a baseball video game, every one of those outcomes is the product of a chain of internal ratings, and the abbreviations attached to those ratings are the language the engine uses to describe skill. Among the dozens of three-letter codes a player can encounter on a card, SA 101 MLB is one that has caused confusion because it borrows shorthand from real baseball stat sheets while living inside a simulated league. The first step toward using it correctly is understanding what the engine actually computes, how the number is normalized, and why the same label can mean different things in franchise, career, and online play.
The breakdown below covers two audiences. Players will see how to read the number on a card, how it changes outcomes, and how it lines up against contact, power, and plate discipline. Designers and technical artists will see how a stat of this shape is modeled inside a baseball sim, what data it depends on, and where naive implementations tend to drift. Both sides need the same foundation, because the rating is only useful when the calculation behind it matches the label the player is shown.
What “SA 101” actually labels in a baseball game
SA, in baseball stat shorthand, usually expands to “sacrifice” in batting tables and refers to sacrifice hits and sacrifice flies, which are outs that nonetheless advance runners. In pitching tables SA is sometimes used for a different concept entirely. The “101” attached to it is rarely part of the underlying value; it is more often a tooltip tier, a code for a specific scouting view, a difficulty band on a progression chart, or a card tier in a roster-building mode. The combined string “SA 101 MLB” therefore tends to mean a beginner-level or reference-tier explanation of the SA stat as it appears in an MLB-licensed baseball game, rather than a single canonical data point.
Because the combined label is ambiguous, the first job for anyone reading it is to check the surrounding context. If a card shows “SA 101” beside a contact rating, it almost always refers to a learning-tier plate-appearance outcome. If a tooltip shows “SA 101” inside a scouting report, it is shorthand for the analyst’s first-pass read on a player’s sacrifice or slugging tendency. Treating the label as one immutable stat is the most common mistake, and it is the one this article is meant to clear up.
How baseball video games present the SA stat to players
Baseball games have a long history of exposing internal ratings to the audience, partly because real baseball fans expect to see numbers on the back of a card. The earliest console baseball titles presented a handful of ratings per player, while modern releases layer in dozens of per-pitch and per-zone attributes. SA sits somewhere in the middle of that history, simple enough to print on a card but specific enough to influence outcomes in a measurable way. The IGN reference for the long-running MLB: The Show series catalogs these abbreviations and pairs each one with a short explanation of what the engine is computing and what the player should expect to see on the card, which is a useful baseline for the rest of this article.
Where SA appears in the UI
In most modern baseball titles, SA shows up in one of three places. The first is a hitter card, alongside contact, power, discipline, and fielding ratings. The second is a season-long stat tracker, where SA accumulates across the schedule the same way it would in a real box score. The third is a scouting report that summarizes how a player performs in specific situations, such as with runners in scoring position, against left-handed pitching, or in late innings. The “101” suffix, where it appears, is usually a tooltip tier and not a separate value.
When SA is on a hitter card, the value is usually a rating on a fixed scale, such as 0 to 99 or 0 to 125 in some legacy engines, that describes how reliably the batter produces a particular outcome relative to league average. When SA is on a season tracker, it is a count of events that have already happened, and the per-game value depends on opportunity, not ability. Mixing the two readings is one of the fastest ways to misjudge a player.
Why the stat is visible at all
Baseball fans are unusually interested in the numbers behind the sport, and that interest extends into the games they play. Designers expose SA partly because it has a real-world equivalent that fans recognize, and partly because the underlying engine has to compute the value anyway to drive outcomes. Hiding the input rating and showing only the output would be technically possible, but it would also cut off a layer of strategic depth, because roster decisions in franchise modes are built around manipulating these inputs over many seasons.
This dual exposure is also the reason “101” sometimes appears. Many games reserve the “101” tier for a beginner explanation, the same way a college course catalog numbers an introductory class. The stat is not “101” because the number is larger or smaller than other tiers; it is “101” because the tooltip is meant to introduce the concept before the player dives into advanced breakdowns. The public community wiki that tracks these abbreviations treats them as gauges rather than as fixed measures of skill, which is the right frame for the rest of this discussion.
The underlying mechanics of an SA-style rating in a sim
Behind the user interface, an SA-style rating is the product of a probability function. A baseball engine does not decide that a player will hit a sacrifice fly; it decides the chance of every possible outcome on a given plate appearance, and SA is one of the levers in that probability table. The exact function depends on the engine, but most share a similar structure.
Inputs to the SA calculation
The inputs to the calculation are the batter’s relevant attribute values, the pitcher’s complementary attributes, the situation, and a small amount of randomness. The relevant batter attributes usually include contact ability, plate discipline, power, and tendency ratings that capture situational behavior. The relevant pitcher attributes include velocity, control, break, and a tendency to throw particular pitch types in particular counts. The situation covers the inning, the number of outs, the runners on base, the ball-strike count, and the park factors. Randomness is needed because real baseball is noisy, and any sim that removes noise becomes obviously artificial.
For the SA outcome specifically, the engine usually weights two of those inputs more heavily than the others. The first is the batter’s tendency to make contact, because a sacrifice outcome requires some kind of batted ball to occur. The second is the batter’s launch-angle profile, because sacrifice flies and bunts depend on the ball going up or going soft in a way the batter intends. Power alone is not enough; a player can hit the ball very hard and still produce a normal flyout, so the engine separates raw exit velocity from situational intent.
How the value is normalized
Once the engine computes a raw probability, it is normalized against league average so the value fits on the same scale as the other ratings on the card. A 75 SA rating in a 0 to 99 scale should mean roughly the same thing across two players, even if one plays in a hitter’s park and the other in a pitcher’s park. Park factors, league difficulty, and roster strength are usually folded in as multipliers before normalization, so the displayed number is a projection of expected performance in an average environment rather than a measurement of past performance.
Normalization is also where the “101” tier, if it is a tier and not a tooltip, becomes meaningful. Some engines display a base rating of around 50 for a league-average player, then layer a small modifier on top to express above- or below-average behavior. A 101 reading in such a system usually represents league average plus one standard deviation of above-average performance, which is a comfortable target for a starter and a stretch goal for a fringe roster piece.
Why developers expose this kind of detail
From a design standpoint, exposing the rating helps players build a mental model of the engine. From a production standpoint, it lets the QA team verify that the rating is doing what the designers claim. A stat that is hidden but misbehaving is much harder to catch than a stat that is visible and obviously wrong. A stat that is visible and right, on the other hand, is a free tutorial that the community will write about on its own, which is why the public stat references exist in the first place.
That is also why a beginner-level label such as “101” sometimes appears in the game. The development team knows the rating is interesting, but they also know most players will look at it once and move on unless the tooltip is friendly. Treating the tooltip as part of the design surface, not a throwaway, is one of the small things that separates a polished baseball title from a competent one. The same community-driven references frame these stats as gauges rather than guarantees, and the UI should match that framing.
Reading SA correctly in different game modes
The same SA rating can change meaning depending on the mode a player is using. Franchise, career, exhibition, and online head-to-head each treat the underlying attributes slightly differently, and the label can be misleading if the player does not adjust. The sections below go through the four most common modes and call out where the rating carries over cleanly and where it does not.
Franchise and season-long modes
In a franchise mode, SA accumulates over the course of a simulated season. Because the sim runs many games in a short time, the engine usually smooths the per-plate-appearance probability function to avoid runs of extremely lucky or unlucky outcomes. The visible rating, in that case, is a long-run expectation rather than a guarantee. A player with a 90 SA rating in a franchise sim will produce more sacrifice outcomes per 600 plate appearances than a player with a 60, but the variance around that average is large enough that one season is not a reliable sample.
Franchise modes also expose player development, which means the SA rating itself can change over the course of a career. Young players usually start near league average, peak in their late twenties, and decline as their attributes decay. The SA rating is one of the attributes that tends to follow that curve, because it is tied to plate discipline and contact skill, both of which erode with age. The split-stat views that ship with most franchise modes are a useful way to spot that curve early, since the per-park and per-handedness breakdowns will show the decline before the overall line does.
Career and player-locked modes
In a player-locked career mode, the SA rating of the user’s created player is usually the one the player will watch most closely. In these modes, the rating is often a function of training choices, archetype selection, and equipment loadouts, and the engine rewards specialization. A player built as a contact-first slap hitter will have a different SA trajectory than a player built as a three-true-outcomes slugger, and the rating tool will reflect that difference over time.
Because the player-locked mode is more sensitive to per-plate-appearance variance, the engine often shows a secondary number alongside the rating. That secondary number is usually a rolling average of the last few games, or a confidence interval around the rating, and it gives the player a sense of whether the rating is performing as expected. The “101” tier, in this mode, sometimes flags a tool that should be read as a confidence interval rather than a single value.
Online head-to-head and ranked play
In online play, the engine cannot rely on a long-run average to mask variance, because the player on the other side of the network is actively trying to exploit any weakness. The SA rating, in that context, is less important as a stat and more important as an input to the per-pitch decision tree. A batter with a high SA tendency is one the pitcher will try to attack differently, perhaps by pitching up in the zone to induce a pop-up rather than a deep fly ball.
Because online play is so sensitive to per-pitch decisions, the engine often exposes more granular inputs in this mode. A player who only ever looked at the SA rating on a card may be surprised to find a separate tendency slider, a clutch modifier, and a pitch-type-specific adjustment in the online loadout screen. Treating the rating as a single number is fine in single-player, but it leaves a real edge on the table in competitive play, and the community write-ups tend to flag those granular inputs as the real skill ceiling.
SA 101 MLB in the wider stat ecosystem
SA does not exist in isolation. It sits inside a network of related ratings, and its meaning depends on the values around it. The table below maps the most common neighbors of SA and explains what each one contributes to the engine.
| Stat | What it tracks | How it interacts with SA | When to trust it more than SA |
|---|---|---|---|
| Contact | Ability to put the bat on the ball | Provides the base probability that any batted-ball outcome occurs | When the outcome is a strikeout rather than a fly ball |
| Power | Exit velocity and home-run tendency | Amplifies the upside of contact when contact occurs | When the outcome is a home run rather than a sacrifice |
| Discipline | Patience at the plate, walk rate, chase rate | Adjusts the count before the plate appearance reaches a batted-ball state | When the batter is unlikely to put the ball in play at all |
| Clutch | Performance in high-leverage situations | Multiplies the SA probability in late innings and with runners on | When the situation is the dominant factor in the outcome |
| Platoon tendency | Difference against same- versus opposite-handed pitching | Adjusts the SA probability based on pitcher handedness | When the matchup is unusual, such as a lefty specialist |
For readers who want a second pass on the same vocabulary, the public community wiki maintains a parallel table of these abbreviations and how each one is typically presented in a baseball game UI. The table there is short on purpose, because most of the value is in the tooltip inside the game itself, and the wiki treats the numbers as gauges rather than precise measurements of skill.
The table above is not exhaustive, but it illustrates the main point: SA is one lever, not the lever. A batter with elite contact and elite power but a low SA rating will still produce plenty of sacrifice outcomes, because the conditions for those outcomes are met often. A batter with elite SA but poor contact will rarely reach the situation in which SA matters, because too many plate appearances end in a strikeout or a walk.
When the underlying rating is more useful than the season stat
There are situations in which the underlying rating is more informative than the accumulated season stat. A rookie in his first month will have a small sample, and the season stat will be noisy. A veteran returning from injury will have an old sample that no longer reflects his current attributes. A player traded mid-season will have split his stat line across two teams, and the league-average context will have changed. In all three cases the rating is a better predictor of future performance than the stat line, because the rating is recalibrated each time the player is loaded into a new environment. The community references that catalog these abbreviations flag the same point: in small samples, the input rating carries more signal than the output stat line.
When the season stat is more useful than the rating
There are also situations in which the accumulated stat is more informative. A veteran who has played ten seasons at a steady rating will have a stat line that reflects his true ability more precisely than the rating, because the rating is a discrete input and the stat line is a continuous output. A player who has played through injuries and slump cycles will have a stat line that captures the resilience the rating cannot model. A player in a park with extreme factors will have a stat line that reflects the local context the rating does not know about.
The pattern is the same one that appears in real baseball analytics. Inputs are better than outputs when the sample is small, and outputs are better than inputs when the sample is large. Baseball video games make the comparison easier than real life, because both the input and the output are visible on the same screen, and the public stat references explicitly warn that split stats are noisy in small samples.
Developer-side considerations when implementing an SA-style stat
For a technical designer or engineer building a baseball sim, an SA-style stat is a small but representative design problem. It involves probability, normalization, UI, and balancing, and it is a good case study for how to think about all the other situational ratings in the game. The same community references that document SA also document the related attributes, which makes them a useful cross-check during implementation. A solid grounding in the abbreviations cataloged on the public Stats 101 reference page is enough to start tuning an attribute of this shape against the rest of the engine.
Modeling the per-plate-appearance distribution
The first decision is whether to model the outcome as a discrete event or as a continuous value. A discrete model treats the plate appearance as one of a fixed list of outcomes, such as strikeout, walk, single, double, home run, or fly out, and assigns a probability to each. A continuous model treats the outcome as a distribution over a numerical value, such as exit velocity, and then categorizes the value into the standard outcomes. The discrete model is simpler to tune, but the continuous model produces more realistic correlations between outcomes, because a high exit velocity is more likely to be a home run and a low exit velocity is more likely to be a grounder.
For SA specifically, a continuous model is usually the better choice, because the outcome depends on the launch angle, which is a continuous value. A discrete model that treats sacrifice outcomes as a separate probability bucket will not capture the realistic fact that sacrifice flies and deep flyouts are part of the same launch-angle distribution as home runs. Modeling the distribution as a whole, then carving out the sacrifice slice, produces more coherent behavior and lines up with how the public references describe the relationship between launch angle and outcome.
Separating tendency from ability
Another design decision is whether the SA rating represents ability, tendency, or both. In real baseball, a player’s ability to hit the ball hard is separate from his decision to swing early in the count or to take a pitch. A baseball sim that conflates the two will produce a player who is “good at sacrifice” in a way that bleeds into his other outcomes. Separating the inputs lets the engine apply the SA tendency only in the situations where it makes sense, which is more realistic and easier to balance.
The cleanest separation is to model the ability as the launch-angle and exit-velocity profile, and the tendency as a per-count decision tree that biases the swing choices. The SA rating then becomes a multiplier on the relevant slice of the launch-angle distribution, plus a small adjustment to the swing-decision tree, and the two pieces can be tuned independently. Most public references frame the rating as a tendency rather than a pure ability, which matches this split.
Calibrating against real data
A baseball sim that wants to feel realistic needs to compare its outputs against real-world data. The reference dataset is usually the public play-by-play feed from the league, plus the Statcast data on exit velocity and launch angle. Calibrating the engine against this data is a multi-week process that involves running the sim in batch mode, comparing the output distributions, and tuning the inputs until the distributions match within a tolerance.
For the SA stat specifically, the calibration target is the league-wide rate of sacrifice outcomes per plate appearance, broken down by count, by park, and by pitcher handedness. The engine should reproduce those rates within a few percent, and the residuals should be small enough that they look like noise rather than systematic bias. A useful sanity check is to plot the simulated launch-angle distribution against the real one, because a sacrifice outcome is just a slice of that distribution.
Validating the rating in QA
Once the engine is calibrated, QA needs to verify that the visible rating matches the simulated output. The classic test is a long-run simulation in which a fixed roster plays a fixed schedule, and the per-game stat lines are compared against the projection from the rating. If a 90 SA hitter produces significantly more or fewer sacrifice outcomes than the rating predicts, the rating is mislabeled and the engine is producing a result the player cannot reason about.
A second test is a comparison across difficulty levels. The engine should produce the same rating, but the user-side modifiers should make the outcomes more or less favorable. If the rating changes with difficulty, the player is being given a moving target, and any tool that recommends roster moves will be wrong. A static rating, a difficulty modifier, and a clear separation between the two is the design that survives QA and that matches the public framing of these stats as gauges rather than guarantees.
Short checklist for technical designers
When a designer is asked to add an SA-style stat to a baseball sim, the practical checklist usually looks like the following. First, define the data sources and the resolution. Second, model the underlying distribution as a continuous value, not a discrete bucket. Third, separate ability from tendency. Fourth, calibrate against the public data. Fifth, expose the rating in the UI with a tooltip that explains the scale. Sixth, add the rating to the franchise and progression systems. Seventh, validate in QA with a long-run simulation. Eighth, document the stat for the community team so the tooltips and the marketing copy agree with the engine.
None of those steps is glamorous, and most of them are easy to skip on a small project. They are also the steps that determine whether the stat is a number on a card or a believable part of the game. A polished baseball sim is usually a polished collection of these small decisions, repeated for every attribute, and the public references that catalog them tend to be written by players who have noticed which studios got the small decisions right.
How players can use the SA stat to make better decisions
For the player side of the audience, the practical value of understanding the SA stat is that it improves roster decisions, training choices, and in-game decisions. A working mental model of the rating is worth more than a memorized tier list, because the model survives roster updates and balance changes. The community references that pair each abbreviation with a short description are a useful backup, but the in-game tooltip is the source of truth for any given build.
Roster construction in franchise mode
In a franchise mode, the SA stat is one of the inputs to lineup construction. A team that wants to manufacture runs with small ball will value a high SA rating in the middle of the order, because the players will be in position to drive in runs with less than a hit. A team that wants to score with power will value a high SA rating less, because the players will score on home runs and doubles rather than on sacrifice outcomes. There is no universally correct answer, and the right balance depends on the rest of the roster.
A practical heuristic is to look at the bottom of the order, where the worst hitters play, and ask whether a high SA rating will turn automatic outs into productive outs. If yes, the rating is worth the lineup slot. If the rest of the order already produces plenty of runs, the rating is luxury. If the rest of the order struggles to get on base, the rating is a waste, because the batter will not have a runner to advance.
Training and archetype choices in player-locked modes
In a player-locked mode, the SA stat is one of the attributes that training programs can target. The right training plan depends on the archetype. A contact-first archetype will want a moderate SA rating, because too high a tendency will pull the swing out of the contact-friendly launch-angle band. A power-first archetype will want a low SA rating, because the goal is to swing for the fences. A balanced archetype will want the SA rating tuned to the lineup context, which usually means a slight lean toward situational hitting.
The mistake to avoid is to over-train the SA rating in pursuit of a single value, because the rating is a slice of a larger distribution. A 99 SA rating does not produce 99 sacrifice outcomes per game; it produces a probability adjustment that, applied to the relevant slice of the distribution, increases the rate of sacrifice outcomes by a few points per 100 plate appearances. Training a rating to its ceiling is rarely worth the opportunity cost, and the public references frame the rating as a tendency rather than a guarantee for the same reason.
In-game decisions in online play
In online play, the SA stat informs pitching decisions. Against a high-SA batter, the pitcher should avoid pitches in the heart of the plate on hitter’s counts, because the batter is more likely to lift the ball and produce a deep flyout or a sacrifice fly. Against a low-SA batter, the pitcher can attack the zone more aggressively, because the worst-case outcome is a groundout or a strikeout rather than a productive fly ball.
Defensive positioning is also affected. Against a high-SA batter, the outfield should play slightly shallower to cut off the sacrifice fly, accepting the risk of a ball dropping in front. Against a low-SA batter, the outfield can play at standard depth, because the worst-case outcome is a single rather than an extra-base hit. These adjustments are small in the abstract, but over a long online session they are the source of a real edge, and the community write-ups on these tendencies are usually written by players who have tracked the per-pitch outcomes across many ranked games.
Common misconceptions about SA 101 MLB
Even experienced players and developers carry a few misconceptions about the SA stat, and a few of them are worth addressing directly. The list below is not exhaustive, but it covers the ones that show up most often in community discussion.
- SA 101 is not a single canonical stat. The label is a tooltip tier or a beginner-level reference, and the underlying value depends on the engine and the mode.
- SA is not a measure of a player’s overall hitting ability. It is a tendency multiplier on a slice of the launch-angle distribution, and it should be read alongside contact, power, and discipline.
- Higher SA is not always better. A team that scores on home runs will undervalue the rating, and a team that manufactures runs will overvalue it. Context determines the optimum.
- The accumulated season stat is not a better predictor of future performance than the rating in small samples. The rating is recalibrated each season, while the stat line carries the noise of the past.
- The “101” tier is not a difficulty level. It is a learning tier in the tooltip system, and the actual difficulty of the league is set elsewhere in the engine.
- A high SA rating does not guarantee a sacrifice outcome on any given plate appearance. The rating is a probability adjustment, and the variance around it is large.
These misconceptions are common because the SA stat looks like a real baseball stat, and the intuition from real baseball does not always transfer to the simulated environment. The public stat references that catalog these abbreviations make the same point: the numbers are gauges, not guarantees, and a small dose of skepticism about any label that includes “101” is the right starting point for both players and developers.
Comparison of SA with related hitting ratings
The table below summarizes how SA relates to the most common hitting ratings a player will see on a card, and what each one is actually doing in the engine.
| Rating | What it represents | How it relates to SA |
|---|---|---|
| SA | Tendency to produce sacrifice-type outcomes | Multiplies the relevant slice of the launch-angle distribution |
| Contact | Probability of making any contact at all | Must be high for SA outcomes to occur on a regular basis |
| Power | Exit velocity and home-run frequency | Pulls the launch-angle distribution upward, which can crowd out SA |
| Discipline | Patience at the plate and walk rate | Determines whether the count reaches a batted-ball state |
| Clutch | Adjustment in high-leverage situations | Multiplies the SA probability when runners are on in late innings |
The two tables together are the simplest way to keep the vocabulary straight while playing. Contact, power, and discipline set the conditions under which any batted-ball outcome can occur, and SA, clutch, and platoon tendency decide which slice of the distribution the outcome actually falls into. None of the ratings is a guarantee, and reading them as a group is what separates a deliberate roster move from a guess.
Frequently asked questions
What does SA 101 MLB actually stand for in a baseball game?
SA is an abbreviation whose meaning depends on the table it appears in. In batting tables it usually means sacrifice outcomes, and in some advanced breakdowns it can refer to slugging-related context. The “101” portion is most often a tooltip tier that signals a beginner-level explanation, and it is not a separate stat. The combined label is best read as a beginner reference to the SA stat, not as a single canonical data point.
Is SA the same stat as slugging percentage?
No. Slugging percentage is a real baseball stat that measures total bases per at-bat, and it is a continuous value calculated from the box score. The SA stat in a baseball video game is usually a tendency rating on a fixed scale, and it represents the probability of producing sacrifice-type outcomes on a given plate appearance. The two are related in spirit, because both are concerned with how the ball comes off the bat, but they are not the same value.
Where does the SA stat appear in the user interface?
The SA stat usually appears in three places: on a hitter card alongside contact, power, and discipline; in a season-long stat tracker that accumulates across the schedule; and in a scouting report that summarizes situational performance. The exact placement depends on the game, but the rating is almost always visible to the player because the engine needs to compute it anyway.
How is the SA rating calculated in a baseball sim?
The rating is a probability adjustment applied to a slice of the launch-angle distribution, normalized against league average and exposed on a fixed scale. The underlying engine models exit velocity and launch angle as continuous values, then carves out the slice that corresponds to sacrifice outcomes. The rating multiplies the probability of landing in that slice, and the resulting output drives the visible outcome on the field.
Should a player train the SA stat in a career mode?
The right answer depends on the archetype and the lineup context. A contact-first archetype benefits from a moderate SA rating, a power-first archetype usually wants a low rating, and a balanced archetype tunes the rating to the lineup slot. Over-training the rating to its ceiling is rarely worth the opportunity cost, because the rating is a probability adjustment rather than a guarantee.
How do developers validate that the SA rating is implemented correctly?
The standard test is a long-run simulation in which a fixed roster plays a fixed schedule, and the per-game stat lines are compared against the projection from the rating. A second test compares the simulated launch-angle distribution against the public Statcast data. A third test checks that the rating does not change with difficulty, because the user-side modifiers should be separate from the underlying attribute.
Does the SA stat matter in online head-to-head play?
It matters in a different way than in single-player. In online play the rating is less important as a stat line and more important as an input to the per-pitch decision tree, because the opponent is actively trying to exploit any weakness. Pitching around a high-SA batter and shifting the outfield shallower are real edges, even if the per-game stat line is small.
Why do some games label the tooltip “101” and others do not?
The “101” tier is part of the tooltip system, not the underlying stat, and it is the design team’s way of signaling that the explanation is introductory. A game that wants to expose every rating to every player will use the “101” tier liberally. A game that wants to gate the explanation behind progression will not. Either choice is defensible, and the right answer depends on the audience the game is designed for.
Can the SA stat be wrong even if the rating looks right?
Yes. A miscalibrated distribution, a missing park factor, or a conflated ability-tendency pair can produce a rating that looks plausible but produces the wrong outcomes. The QA process is designed to catch these cases, but a small bias in the launch-angle distribution can take many seasons to surface. A regular calibration pass against the public data is the cheapest insurance against this kind of drift.
Where should a new player start when learning the SA stat?
The right starting point is the in-game tooltip, cross-checked against a public reference and a few hours of observation. The tooltip explains the local meaning, the reference explains the historical context, and the observation confirms that the rating produces the outcomes the tooltip promises. Reading the tooltip in isolation is the most common source of confusion, and a small amount of cross-checking goes a long way.
