# Sandbagging & Sniping Detection Guide > A practical guide for sales leaders to spot reps who systematically under-forecast to look like heroes at quarter-end, and the specific coaching conversation that fixes the behavior without torching trust. Free guide for sales leaders. Topic: [Pipeline, Deal Management & Forecasting](https://www.peerlab.ai/library/topic/pipeline-deal-management-forecasting/). Published by PeerLab in the [Sales Enablement Resource Library](https://www.peerlab.ai/library/). Source: https://www.peerlab.ai/library/sandbagging-sniping-detection-guide/ ## How to use it Run the 6 detection signals against your CRM's forecast-vs-actual history each quarter to identify patterns, then use the coaching conversation script as a private, evidence-led 1:1, never a public callout, paired with the structural comp-plan check. ## What's inside - Working definitions distinguishing sandbagging, sniping and legitimate conservatism - 6 statistical and behavioral detection signals with how-to-check instructions for each - A worked example applying all 6 signals to one rep's data - Root-cause analysis of why sandbagging happens (comp design, prior burns, culture) - Word-for-word private coaching conversation opener and 5-step structure - What NOT to do list (punitive responses that backfire) - Structural comp-plan and process fixes beyond the 1:1 - Quarterly re-run cadence across the whole team, not just the flagged rep ## Definitions - **Sandbagging**: systematically forecasting a deal lower (or later) than the evidence supports, so it "surprises" the forecast in the rep's favor at period-end, making the rep look like they beat their number through skill rather than manipulation of the call. - **Sniping**: a close cousin: holding a deal that's actually ready to close and deliberately delaying signature until early in the *next* period, so it "counts" toward next period's number instead of helping the team hit a stretch number this period (or to build a cushion for a slower month ahead). - **Legitimate conservatism**: forecasting cautiously because the evidence genuinely doesn't support a higher category yet. This is healthy and should never be coached away: the distinction from sandbagging is whether the evidence, if honestly read, actually supports a higher category. ## 6 Detection Signals ### 1. The "miraculous beat" pattern Rep consistently forecasts Commit/Best Case at 60-70% of what they actually close, quarter after quarter: and the "surprise" upside always comes from deals that, in hindsight, had all the hallmarks of Commit (verbal agreement, signature in process) weeks before period-end. **How to check**: pull the rep's forecasted number vs. actual close for the last 4-6 periods. A rep who beats their own forecast by 30%+ every single period: not occasionally, every time, is not being lucky. ### 2. Late-quarter clustering with early-quarter readiness A disproportionate share of the rep's closed-won deals sign in the last 3 business days of the period, but the CRM activity history shows verbal commitment or contract-out status 2-3 weeks earlier. **How to check**: for each closed-won deal, compare "date contract was sent" to "date signed." A pattern of 15-20+ day gaps between contract-out and signature, concentrated at period boundaries, is a sniping signal: that gap is usually a choice, not a legal delay (cross-check against actual legal/procurement bottleneck reasons first). ### 3. Category "stuck" at Pipeline/Best Case despite Commit-level evidence Deal has a documented verbal commitment and paperwork in process, but the rep keeps it in Best Case (not Commit) for multiple consecutive forecast calls without a defensible reason. **How to check**: cross-reference CRM activity notes against forecast category history. If the notes describe Commit-level facts but the category says Best Case for 2+ calls running, ask why directly on the call. ### 4. First-period-of-next-quarter spike A rep's month-1 bookings are consistently much higher than their team average relative to month-3: every quarter, like clockwork. **How to check**: chart bookings by month-in-quarter (M1/M2/M3) per rep across several quarters. A rep whose M1 is 40%+ of their whole quarter, repeated pattern, is worth a direct conversation. ### 5. Understated deal size, not just category Some reps under-forecast the *amount*, not just the *timing*, logging a deal at a partial value ("just the first phase") when the actual signed contract includes phases 2-3 already agreed verbally. **How to check**: compare forecasted deal amount to actual signed contract amount for the rep's last 6-8 closed deals. ### 6. Reluctance to update mid-period When you ask a rep mid-quarter "any upside I should know about?" and they consistently say no, but then close meaningfully above what was forecast, that's a behavioral tell independent of the CRM data. ## Worked Example Rep A forecasts $380k Commit+Best Case for Q3, actually closes $510k (34% beat). Same pattern in Q1 (31% beat) and Q2 (38% beat). Contract-out-to-signature gap on 5 of their 7 Q3 deals is 12-20 days, and all 5 signed in the final week. Category history shows 3 deals sat in Best Case for 3+ consecutive forecast calls despite notes describing verbal agreement and paperwork status. This is a strong, multi-signal sandbagging pattern: not one data point, several converging. ## Why It Happens (root causes, not just symptoms) - Comp plan rewards beating forecast more than hitting it accurately (accelerators kick in on "upside surprises") - Rep was burned before by a deal getting pulled forward or re-negotiated when leadership saw it as Commit too early - Team culture treats forecast misses as career risk but forecast under-promising as a virtue ("under-promise, over-deliver") - No consequence has ever been attached to consistent sandbagging, so the incentive to do it has never been countered ## The Coaching Conversation Do this 1:1, privately, data in hand, never in a team forecast call. **Opener**: > "I want to talk about your forecast accuracy, not your results, your results have been great. Looking at the last three quarters, you've beaten your own forecast by 30%+ every single time. That's not luck, and I don't think it's an accident. I think you're forecasting conservatively on purpose. I want to understand why, because it actually makes it harder for me to plan the business, and it might be costing you upside you don't realize." **Structure**: 1. Ask, don't accuse: "Walk me through how you're thinking about categorizing deal X." Let them explain their logic. 2. Name the specific evidence gap: "The notes on this deal describe a verbal commitment three weeks before you moved it to Commit. Help me understand the gap." 3. Surface the real incentive: "Is there a reason you'd rather this look like an upside surprise than a hit number?" Often the honest answer is fear of overcommitting, not gaming. 4. Reframe the upside: explain that an accurately forecast Commit that closes on time is scored the same as (or better than, in most well-designed comp plans) a surprise beat, check your own comp plan and be honest about this. 5. Set the go-forward standard: "From now on, I want your forecast categories to reflect the evidence checklist, not your gut on what feels safe to promise." ## What NOT to Do - Don't publicly call it out in a team forecast call: it reads as an accusation and destroys trust in front of peers - Don't punish accurate forecasting that happens to include some conservative deals: you'll teach reps to hide information more, not less - Don't change the comp plan unilaterally without also fixing the coaching conversation: a structural fix without the conversation just teaches reps a new way to game the new structure ## Structural Fixes Beyond the 1:1 - Adjust comp accelerators so hitting forecast accurately is rewarded at least as well as beating it - Make forecast accuracy (not just attainment) a visible, tracked metric in 1:1s and reviews - Require evidence-based category justification on every forecast call, for every rep, so the standard is uniform and not perceived as being singled out ## Follow-Up Cadence Re-run the 6 detection signals every quarter for the whole team, not just the flagged rep: sandbagging is often a team-culture response to comp design, and the pattern usually shows up in more than one person once you look.