For MT241 Unit 2, expected goals is defined input by input, then limited: the exercise states which club decisions the measure can inform and which it cannot. Searches like "mt 241 unit 2 assignment example", "mt241 unit 2 sample" and "mt241 unit 2 example" land here.
What a finished MT241 Unit 2 metric definition exercise looks like
Four pages built around a definition table, one worked example and a short comparison. The table runs seven rows: population, unit of measure, inputs, output, what the measure adjusts for, what it leaves out and the decision it serves. Population is every unblocked 5-on-5 attempt, since a blocked shot's recorded location is where it was blocked, not where it was taken. Inputs are distance, angle, shot type and flags for rebounds and rushes. Each attempt receives a probability, summed per player or team; a typical public model might score a twelve-foot slot wrist shot near 0.16 and a point shot near 0.02. The worked example takes a winger with 14 goals on 8.9 expected. The comparison sets expected goals beside raw attempts and actual goals for the club's season.
How a MT241 Unit 2 example is structured
Nothing is computed until the definition is complete, because the unit's question is what the measure adjusts for, and that answer lives in the definition. Population is fixed first, with the reason blocked attempts are dropped stated rather than assumed. Inputs follow, each named with what it stands in for: distance and angle for how much net the shooter sees, the rebound flag for a goaltender caught out of position. The row on omissions carries most of the argument. Location-based public models do not see a cross-ice pass before the shot, a screen in front of the goaltender or the shooter's own finishing skill, and a scorer's recorded location can be off by several feet. The worked example then applies those limits to the winger. The table's last row pairs the measure with the club decisions it suits, and names one it does not.
Unblocked attempts only
Shots on goal plus missed shots make the population. Blocked attempts drop out because the league records them at the point of the block, which would score a point shot stopped in the slot as a slot chance.
Five inputs, each a stand-in
Distance and angle stand for visible net, shot type for release speed, the rebound flag for a goaltender moving across, and the rush flag for a defense not yet set. Each stand-in is named, since each can fail.
What the model cannot see
A cross-crease pass that pulls the goaltender, bodies screening his view and the shooter's own skill are absent from location-based public models. A recorded location a few feet wrong shifts a slot shot's value noticeably.
Fourteen goals on 8.9 expected
The winger beat his expected total by 5.1 goals on 142 unblocked attempts. Luck alone would produce a gap that large for about one player in twenty-five, so one season suggests finishing skill without establishing it.
Decisions it fits, one it does not
Expected goals can show whether a line creates chances at a sustainable rate, or whether a hot month rested on shot quality. It cannot, from one season, price a shooter's finishing for a contract, and the table says so.
Where marks go in MT241 Unit 2
Treating expected goals as a black box, a number the site supplies, earns the least in MT241, because the Unit 2 prompt typically asks what the measure adjusts for and a definition without inputs cannot answer it. Claiming blocked shots are included, or that a location-only model knows about pre-shot passing, draws deductions from instructors who use these tools. Leaving the omissions row thin is its own weakness: a definition listing only what the measure does reads as promotion. Worked examples that read one player's excess goals as proof of skill contradict the definition's own limits. Some sections also expect the measure compared with a simpler one, shot attempts or goals, so its added value is shown rather than asserted. Probabilities quoted with no model named look invented.
Get a MT241 Unit 2 example written to your instructions
Unit 2 prompts name different measures: expected goals, points per possession, WAR, completion percentage over expected. Tell us which one yours assigns, attach the rubric, and mention any data file provided. A first custom exercise, free and ready inside 24-48h, defines the measure's population, inputs and blind spots and pairs it with the decisions it can support.
MT241 Unit 2 questions, answered
What is expected goals in hockey?
A measure that assigns each unblocked shot attempt the probability it becomes a goal, based on features such as distance, angle, shot type and whether it followed a rebound or a rush, estimated from many past shots. Summing those probabilities gives the goals a player or team would score from its chances at average finishing, which separates shot quality from shot volume.
Why do different sites report different expected-goals totals?
Each builds its own model, with different inputs, different handling of rebounds and rushes and different seasons of training data. Totals for the same team can differ by several goals across a season. A paper should name the source it used and never mix figures from two models, which the example avoids by drawing every value from one site.
Can the same exercise be done for a basketball or football measure?
Yes, and many sections allow the choice. Points per possession needs its possession estimate defined, including how free throws and offensive rebounds are treated; completion percentage over expected needs its inputs listed like any model. The structure carries over, population, inputs, adjustments, omissions and the decisions the measure fits, and only the sport's own counting rules change.