What the forecast must decide
- Define the forecast target and cut-off date.
- Build a baseline from comparable events.
- Layer event drivers and produce scenario ranges.
Target, comparables, decision output
Target
Choose whether you forecast paid tickets, turnstile attendance, gross demand, or revenue; they are not interchangeable.
Comparable set
Day of week, opponent, competition, holiday, lead time, and inventory can define comparable events.
Decision output
The model should feed a decision: release inventory, adjust channel spend, open sections, or change staffing.
Forecast five hypothetical events
Build a forecast table for five events with baseline demand, three drivers, current pace, base/upside/downside forecasts, and a recommended action. Add a column with the assumption most likely to be wrong.
Forecast at the level where a decision changes
A total attendance forecast can be too broad if the real decision concerns staffing, inventory, or pricing by section. On the other hand, forecasting every seat individually may create complexity without better action. Choose the level of detail that matches what the business can change.
Document when the forecast is made. A model seven days before an event has different information from a model six weeks out, so accuracy should be compared at similar horizons. Include a simple baseline such as the last comparable event or average pace of sales.
Turn the output into a plan
Produce a base forecast plus low and high bounds. Next to each scenario, write what changes operationally: staffing, food and beverage inventory, paid media, release of seating blocks, or pricing review. This is the point of the exercise. A forecast without an attached decision is just another number.
Make the action threshold explicit
A forecast becomes useful when somebody knows what to do with each range. Suppose the expected demand is 12,000–14,000. At what point do you open another section, add security, change paid media, or release inventory held for partners? Put those thresholds beside the forecast rather than leaving the operational team to invent them later.
Then run a simple sensitivity check. Change one driver at a time—price, lead time, weather assumption, opponent category—and record which one changes the decision. This shows where additional research has value and where precision would not affect the action.
Connect the output to forecasting and keep the model auditable. A good portfolio forecast demonstrates not only an estimated number but also the decision rule, the uncertainty and the review after the event.
Tie forecast error to an operational choice
Before polishing the submission, give the work to a skeptical reviewer with three questions: What decision is this supposed to support? Which assumption is doing the most work? What would make the recommendation change? If those answers are hard to locate, the project needs editing before it needs another chart.
Use Case: Evaluate a Sponsorship Package to revisit the weakest part of the method and Forecasting for Sports Business to check whether the output is appropriate for the role that would receive it. Add one edge case that your first version handles badly. A useful case study shows the correction instead of pretending the first attempt was clean.
Finish with a short handoff note: source, definitions, exclusions, refresh steps, and the next decision date. Compare the result with KPI Design. The final portfolio piece should be understandable by someone who did not watch you build it, because that is much closer to how analytical work is reviewed inside a team.
What a demanding reviewer should ask of this case
Treat the case as if it were being handed to a colleague on a busy day. The reader should be able to identify the decision, the source of the evidence, the main assumption, and the requested action without watching a presentation. If those elements are scattered across tabs or paragraphs, reorganize the work before adding more analysis.
Run one adverse scenario. Change a high-impact assumption or introduce an edge case that the first version handles poorly. Use Case: Evaluate a Sponsorship Package to strengthen the method, then state how the recommendation changes—or why it does not. This is where a case study starts to demonstrate judgment rather than only execution.
Ask a second person to review the artifact with a timer. In the first two minutes, can they tell what happened, why it matters, and what should happen next? Compare their interpretation with Forecasting for Sports Business. If the reviewer focuses on a chart you considered secondary, either move the chart or make the decision hierarchy clearer.
The final handoff should include a source note, definitions, exclusions, refresh instructions, and one limitation that remains unresolved. Use KPI Design to connect the case to the next skill or role. A strong portfolio case does not pretend uncertainty disappeared; it shows that the uncertainty was identified and managed.