When Destination Depth Scoring Reveals What Peak Season Maps Hide

Peak season maps are works of art. They are also works of omission. In 2023, the National Park Service reported that 70% of visitors to Yellowstone only saw the lower loop — the one with Old Faithful and the Grand Prismatic Spring. The rest of the park? Invisible. The official map doesn't hide it on purpose; it just prioritizes density. But for the traveler who wants to actually feel a place rather than just check it off, that prioritization is a problem.

Destination Depth Scoring (DDS) is a framework that layers objective metrics onto a destination to reveal what standard maps omit. It doesn't replace maps. It interrogates them. 'A map is a snapshot of what someone wants you to see,' says travel editor Lina Hart. 'Depth scoring is what you see when you turn the map over.' This article walks through the full workflow — from identifying who needs DDS, to running the core process, to diagnosing when it fails. By the end, you'll have a system that cuts through the marketing fluff.

Who Actually Benefits from Destination Depth Scoring?

The overplanned traveler who books everything and sees nothing

You know the type—maybe you are the type. Six cities in ten days. Pre-booked museum slots, timed lunch reservations, a spreadsheet color-coded down to the minute. Peak season maps love these people. They buy skip-the-line passes, fill hotel rooms on sold-out weekends, and leave glowing reviews about how efficient everything was. But efficiency is not depth. I have watched travelers finish a 48-hour Rome itinerary and remember only the airport security line and the price of a cappuccino near the Trevi Fountain. Destination Depth Scoring catches what the map hides: how many minutes you actually stopped moving. How often you changed transit modes. The gap between what you booked and what you absorbed.

That sounds fine until you run your initial score and realize you spent forty percent of your trip inside metro tunnels. The catch is brutal—overplanning inflates your location count but tanks your engagement density. Most teams skip this: they optimize for coverage, not for signal. The DDS framework penalizes you for that. It does not care how famous the sight is; it cares whether you stood still long enough to feel the place.

The slow traveler who wants to stay longer in fewer places

This profile is rarer than you think. Slow travel sells well in blog posts but breaks down fast when real-world constraints hit: visa limits, budget fatigue, the creeping boredom of day four in a small town. However, DDS finally gives the slow traveler a metric that justifies their pace. Instead of saying "we spent a week in Ljubljana because we wanted to," they can point to a depth score that shows diminishing returns per additional day. Wrong order—you do not calculate depth after the trip. You set a minimum depth threshold before you leave, then let it veto destinations that cannot absorb that many days.

We fixed this by running a mock score for a three-night stay versus a six-night stay in the same region. The six-night version dropped by a full point because the area's public transit frequency and walking radius could not support that duration without repetitive loops. That data changed the itinerary. The slow traveler benefits most from DDS because it tells them when to stop—not just where to go. Not yet a common insight, but it should be.

The niche seeker who cares about food, culture, or nature — not just sights

Mainstream trip planning treats every destination like a checklist of monuments. The niche seeker wants to eat where locals eat, find the third-wave coffee roaster, hike the ridge that does not appear on the primary page of search results. Peak season maps actively mislead these people—they highlight the crowded viewpoint, not the hidden trailhead. DDS flips that. When you score for cultural depth, you drop the distance weight on transit time and increase the weight on variety of experience types per square kilometer. A neighborhood with two world-class bakeries, a woodworker's studio, and a community garden scores higher than a district with fifteen souvenir shops and a cathedral. That hurts if you are selling a "top ten attractions" package. But for the niche seeker, it is gold.

Depth scoring does not care about famous. It cares about dense. The two are rarely the same thing.

— field notes from a food-tour operator in Oaxaca, after switching from Google Maps ranking to DDS weighting

The trade-off is real: niche scoring requires better source data. You cannot pull "ceramic studio density" from a generic POI database. You need to curate your own category tags or use open street map filters. Worth flagging—the extra effort pays off when you return a score that actually matches how the traveler will spend their time. The overplanned traveler gets a wake-up call. The slow traveler gets permission to linger. The niche seeker gets a route that rewards curiosity over efficiency. All three groups share one thing: the standard peak-season approach sold them a map that edited out the texture. DDS puts it back.

What Data Should You Settle Before You Score?

Understanding the Five Core Metrics: Proximity, Density, Accessibility, Seasonality, Authenticity

You cannot score depth without initial locking down what depth means in measurable terms. I have watched travelers spend hours building elaborate spreadsheets only to realize they had no baseline for “good enough.” So here is the raw framework. Start with proximity—distance in meters from your lodging to the nearest grocery, café, and park. Under 200 meters for each? That is a baseline win. Density counts walkable points of interest within a one-kilometer radius: anything above 40 yields a dense core, but 15–25 often hides the quiet neighborhoods worth your time. Accessibility measures transit frequency per hour during off-peak windows—fewer than four buses an hour and you will feel stranded. Seasonality is your trap metric: high-season crowds inflate every other number, so calibrate against shoulder-season data when possible. Authenticity remains slippery—I define it as the ratio of independent businesses to chain storefronts within that same kilometer. Above 80% independent? You are in a real place. Below 50%? That seam blows out fast.

The catch is that most platforms serve you peak-season averages. Booking.com shows restaurant density for August in a coastal town—but what does January look like? Wrong order. Not yet. You need the raw numbers from municipal open data or Google Maps timestamps, scrubbed for holiday spikes. One traveler I know scored a village as “deep” using June data, arrived in November, and found three shuttered storefronts. That hurts.

“A depth score built on August numbers is a depth score that lies to you in February.”

— Field note from a solo trip planner, 2024

Gathering Baseline Numbers: Walkability Score, Transit Frequency, Local Business Per Capita

Most teams skip this: you need your own baseline, not a universal ranking. Walkability scores from third-party tools often flatten reality—a 75 in Tokyo behaves nothing like a 75 in rural Portugal. So pull three numbers by hand. First, walkability: time yourself walking from a central point to five essential destinations (grocery, pharmacy, transit stop, café, public square). Under 15 minutes for all five? That is a functional core. Second, transit frequency: count departures between 10 AM and 2 PM on a Wednesday—that dead zone reveals if the system runs for commuters or for residents. Third, local business per capita: divide the number of independent storefronts (no chain prefix) by the residential population of the district. Above 0.014 per person signals a self-sustaining neighborhood; below 0.008 and you are in a tourist corridor or a bedroom suburb.

The tricky bit is that these baselines shift depending on whether you are scoring a city center or a coastal hamlet. I have seen people apply a 0.014 local business ratio to a fishing village of 800 people—impossible math. Instead, calibrate to the settlement’s size. For towns under 5,000 residents, accept 0.005 as a reasonable floor. For cities above 100,000, push for 0.012. The numbers matter less than the delta between your baseline and the destination’s reality. That delta is where insight lives.

Worth flagging—local business per capita breaks completely in places with open-air markets that vendors do not register digitally. You cannot count what does not appear on Google Maps. In those cases, walk the commercial strip yourself for an hour and tally. Imperfect but clear beats a polished spreadsheet that misses the soul of the place.

Setting Your Own Threshold: What ‘Depth’ Means to You

Here is where the personal calibration happens. A solo digital nomad who values quiet cafes and reliable Wi-Fi will set accessibility thresholds higher than a family seeking playground density and grocery proximity. There is no universal depth score—only your depth score. I keep a simple rule: rank each metric on a 1–5 scale, then multiply by a personal weight. Prioritize authenticity at 40% if you are escaping chain tourism. Drop it to 15% if you just need affordable transit. The weights are yours to break.

Should you end up with a score that feels off—and you will—revisit your baseline numbers before touching the weights. Nine times out of ten, the data itself is contaminated by peak-season bias or missing transit routes. Fix the foundation first. Then ask: does this place feel deep to me? If the numbers say yes but your gut says no, walk the block again. The seam between data and instinct is exactly where destination depth scoring reveals what the maps hide.

How to Run the Core Depth Scoring Workflow

Step 1: Overlay the peak-season map with a raw density layer

Pull up your destination during its notorious high season—think Kyoto in November, Barcelona in August, or any national park in July. The official tourism map looks orderly: neat dots for attractions, color-coded districts, maybe a few bus routes. That's the lie. What you want instead is a heatmap of people, not places. I grab geotagged social media check-ins (Flickr, Instagram, or crowd-sourced tracking apps) and dump them over a two-week window of peak dates. The result is always ugly—a pulsing blob of red over the historic core, a secondary hotspot at the airport train station, and thin scattering elsewhere. The catch: this raw density layer includes everyone, including day-trippers who buy a single postcard and leave. So don't trust it yet.

One pitfall I keep seeing: people use annual visitor numbers instead of hourly density. A city that hosts 10 million tourists per year can feel empty outside 11 AM–3 PM—or suffocating inside those hours. Wrong scale, wrong insight. Use hourly or half-day granularity if you can get it. Otherwise your depth score starts with a bias toward the anemic average.

Step 2: Subtract high-traffic zones using visitor volume data

Now you cut away the noise. Overlay official ticket sales, museum turnstile counts, or—if you're scrappy—wait times at major landmarks scraped from reservation APIs. Subtract the zones where volume exceeds what a human can comfortably navigate. I draw a hard threshold: any area where foot traffic hits 80% of its theoretical capacity gets flagged as a subtract zone. That sounds fine until you realize that the Sagrada Familia's surrounding blocks, not just the building itself, become impassable. The depth score should discount not just the attraction but the approach—the gridlocked street vendors, the selfie-stick bottlenecks, the three-deep queue for churros.

What usually breaks first is timing. A plaza that scores high density at noon might be serene at 7 AM. So I don't subtract the zone entirely; I subtract it only for the hours that match your planned visit window. If you're a morning person, the Colosseum vicinity stays in your score. Afternoon arrival? Gone. This nuance separates a usable score from a generic one.

Edge case: festivals. During Diwali in Jaipur or Carnival in Rio, the entire city center becomes a high-traffic zone. Do you subtract everything? No—you shift the reference frame. The depth score in festival conditions measures access to non-festival authentic experiences, which are usually closed or repurposed. So the score drops, and your plan should shift accordingly.

Step 3: Add proximity to authentic experiences (local markets, community events, non-tourist transit)

Here's where depth scoring earns its name. Once you've filtered out the crowded and the overpriced, you add points for what's left: a neighborhood produce market that doesn't appear on any tour company's list, a community volleyball court used by locals after work, a bus route that connects residential areas without passing a single souvenir shop. I score these by distance from your hypothetical stay—walkable (under 15 minutes) adds one point, transit-accessible (under 30) adds half. Not a science, but a useful proxy.

Most teams skip this step. They stop at subtracting crowds and call it depth. But that's just emptiness. True depth requires texture—the kind of place where you overhear an argument in the local dialect or smell spices that aren't blended for export. A concrete example: In Tokyo, I scored Asakusa low on depth because the market street is 80% tourists in peak season. But walk 12 minutes east to the Yanaka district, and the score flips: fewer visitors, a working fishmonger, a shrine where locals actually leave offerings. That 15-minute walk was invisible on the peak-season map.

‘A destination with high depth score doesn't feel empty; it feels inhabited. The difference is often just two blocks and a different time of day.’

— field note from a Nagoya route-planning session, 2023

The tricky bit is data decay. That local market might close for renovation. The community event might be seasonal. I refresh the proximity layer every three months; anything older starts to harm the score's reliability. Worth flagging: do not count restaurant reservations or hotel bookings as authentic experiences—those are commercial transactions, not cultural texture. A ramen shop filled with tourists and one filled with off-duty taxi drivers look identical on OpenTable. You have to cross-reference with local review sites or neighborhood forums. I use Google Maps reviews sorted by language—if 90% are in English, it's a tourist trap. If they're in the local tongue, it's depth. Not perfect, but honest.

When throughput doubles without a matching documentation habit, however skilled the crew, the pitfall is invisible rework: seams ripped back, facings re-cut, and morale spent on heroics instead of repeatable steps.

Operators we shadowed described three distinct failure modes — mis-threaded tension, skipped press tests, and batch labels that never reach the cutting table — each preventable when someone owns the checklist before the rush starts.

A mentor explained however confident beginners feel, the pitfall is skipping the failure rehearsal; says the quiet part out loud — most rework traces back to one undocumented assumption that looked obvious on day one.

Which Tools and Environmental Realities Matter Most?

Open-source mapping stacks versus proprietary platforms — where the detail lives

I have run the same depth score through Mapbox, Google Maps Platform, and an offline OSM extract. The raw coordinates agree within meters. The metadata depth — the stuff that makes a score sing — is where they diverge violently. OpenStreetMap gives you building outlines, footpath classifications, and opening hours that are correct three years ago. Mapbox layers on traffic prediction and visual landmark detection, but you pay per API call. Proprietary platforms tend to data-wash stale info with slick UIs. The catch is that neither side publishes a freshness timestamp for every node. Worth flagging: I once got a perfect depth score for a Tokyo alley that had been demolished for six months — OSM still listed a ramen shop there. The tool choice matters less than your willingness to cross-check a random 10% sample against street view or a local scrawl on Instagram.

Real-world data feeds that break the algorithmic bubble

City tourism boards publish PDF calendars. Local transit APIs return JSON. Instagram geotags carry a when as well as a where — but scraping them is ethically fuzzy and technically brittle. Most teams skip the board PDFs. That hurts. A depth score that ignores a city's annual monsoon closure or a surprise five-day festival will look perfect in January and fail in August. What usually breaks first is event-driven spikes: a marathon shuts down a core scoring zone for a morning; a street market triples the density of photo-worthy stalls. The trick is to pull an event list from at least two sources — a government tourism website and a local blog — and weigh them differently depending on season. Wrong order? You boost a day-trip score for a neighborhood that becomes impassable every Friday afternoon. I have seen a team rebuild their entire pipeline after ignoring a single CSV from the municipal transport office.

Depth scoring without environmental context is like rating a restaurant's menu based solely on the tablecloth.

— engineering lead, transit data integration team, after a failed peak-season batch

Environmental constraints: data latency, seasonal shifts, and the festival that ate your score

Data freshness decays non-linearly. A hiking trail that floods for two weeks each spring will show as "open" in a basemap updated quarterly. That is a latency problem, not a mapping error. Seasonal shifts also warp scoring weights: a café with an outdoor terrace scores high in June but becomes a wind tunnel in December. Event-driven realities are worse because they are abrupt. A single 80,000-person conference can inflate the "density of points of interest" metric to absurd levels — every hot-dog cart within a mile gets geotagged by attendees, and the depth score suddenly ranks the convention center as the most culturally rich spot in the city. We fixed this by adding a temporal decay function that penalizes spikes shorter than 72 hours. The fix was 14 lines of code. The insight took a week of staring at weird score curves. Environmental realities are not noise — they are the signal, once you stop treating them as error bars.

How to Adapt Depth Scoring for Different Travel Constraints

Budget constraint: weighting low-cost authentic experiences higher

Most travelers assume deep scoring and budget travel are natural enemies. Wrong order. The real trick is recalibrating your depth score to punish high-cost, low-return tourist traps while rewarding genuinely cheap immersion. I once watched a friend drop $180 on a guided 'cultural walk' that rushed past twenty souvenir stalls — her depth score cratered because the framework flagged a 12:1 ratio of commerce to conversation. Instead, reweight your formula so street-food interactions, free public gardens, and community-run walking tours carry triple the weight of paid attractions. The trade-off? You might skip the famous cathedral climb ($40, crowded, scripted) for a riverside ramble that costs nothing but time. That hurts if you love certificates of visitation. But the depth signal sharpens: you'll see neighborhoods, not turnstile queues.

The catch: Underweighting paid experiences can accidentally exclude genuinely affordable cultural sites — a $2 museum with no queue still deserves points. Validate by checking whether your lowest-cost entries actually represent time well spent, not just empty wandering.

Time constraint: compressing the score to prioritize density within a short window

Three days in Kyoto. No flexibility. The standard depth framework wants you to spread across a week — but you don't have one. So compress. Shift the scoring lens to reward concentration: how many distinct, non-tourist interactions can you pack within a 2-kilometer radius between breakfast and sunset? I've seen this rescue a frantic 48-hour Lisbon trip: instead of scattering across Belém, Alfama, and the coast, the compressed depth score flagged a single hilly neighborhood where a fado session, a bakery chat, and a tile workshop sat within 800 meters. The depth density hit 0.9 — absurdly high for a tourist. What usually breaks first is transit time: the framework should penalize any movement over 25 minutes because dead hours tank your effective depth per minute. That sounds fine until you realize you skipped the must-see palace three subway stops away. Worth flagging—compression works only if you accept missing the postcard sights.

Not yet satisfied? Tighten further: set a minimum stay threshold of 45 minutes per stop. Anything shorter is a photo sprint, not depth.

Mobility constraint: replacing walkability with wheelchair accessibility and transit step-free access

The standard depth score assumes legs work and cobblestones are charming. For travelers with mobility limitations, that assumption is a wall. Adapt by stripping out 'walkability' entirely — replace it with three hardened metrics: step-free transit access, rest-stop density (benches or cafes with seating every 200 meters), and door-width clearance at entry points. I fixed a friend's score for Barcelona this way: the original framework loved the Gothic Quarter's narrow alleys; the adapted version flagged them as depth-negative because four consecutive blocks had zero wheelchair ramps. Score dropped. She rerouted to the Eixample district — wider sidewalks, metro elevators, and a public market with accessible counters. Depth improved, not despite the constraint, but because the adapted score filtered out inaccessible dead zones most able-bodied guides never notice.

'The first time I ran a mobility-weighted score, I discovered my favorite city was actually a maze of curbs and stairs. The map lied. The score didn't.'

— travel planner, mobility-accessible tourism collective

Real pitfall: transit step-free data isn't always up to date. A station marked accessible might have a broken elevator for months. Cross-reference with local disability forums — they'll tell you which seam blows out.

What to Check When Your Depth Score Feels Off

Rotten data, rotten depth

You run the workflow, glance at the score—say, a 78 for Lisbon in August—and something chafes. The number feels generous, maybe outright wrong. Nine times out of ten the culprit isn't the algorithm; it's the food you fed it. I have seen scores collapse because someone pulled hotel occupancy stats from November to score a June beach run. Off-season averages look serene. Peak-season reality? Triple the bodies, half the shoulder room. That mismatch alone can inflate a Depth Score by twenty points—or crater it. The fix is boring but necessary: check the data vintage on every source. If your crowding metric comes from pre-pandemic census files, bin it. If restaurant density reflects 2022 closures, bin it. Stale data doesn't just skew—it lies.

Debug step: ask the crowd, not the spreadsheet

'A Depth Score built on scraped metadata alone is a painting of a window. You need the actual breeze to know if the room is stuffy.'

— A respiratory therapist, critical care unit

The 'tourist trap' false positive: density without authenticity

High density can look like depth. That's the trap. A destination might score 92 because it has 400 restaurants per square mile, 15 attractions, and endless hotel listings. But walk those streets and you find only chain cafes, souvenir stalls, and three identical "local craft" shops run by the same parent company. The algorithm sees richness. You see a monoculture. The debugging move here: strip out chain brands before running your score. Filter by "independently owned" or "no duplicate corporate registrations." The Depth Score often drops 15–30 points after that single pruning.
That said, not all density is fake. Tokyo's Shinjuku district scores high on raw count and passes the authenticity sniff test—because the density is genuinely layered: tiny ramen bars, electronics bazaars, shrines tucked between pachinko parlors. The difference? Local variance. Run a second metric: category uniqueness ratio. If 70% of your points of interest fall under three category types, something is off. If they spread across twelve categories, the score probably holds. The catch is—most scoring tools don't offer that filter natively. You build it yourself or you live with the false positive.

Frequently Asked Questions About Destination Depth Scoring

Can I automate the scoring completely? (No, human judgment on authenticity is still required)

You can script the data collection — scraping visitor counts, pulling weather windows, mapping transit frequencies — all of that is fair game for code. The tricky bit is that a depth score lives or dies on whether a place feels real versus manufactured. I have seen teams pipe raw numbers into a dashboard and declare a destination “scored,” only to discover the main street was a replica village built for social-media photo shoots. That hurts.

Automation misses texture. It cannot smell the diesel fumes of a real fishing port or catch the way a local vendor rolls their eyes at a tour-bus schedule. You still need a human pass — even if it is just a fifteen-minute video call with a guide on the ground — to sanity-check the vibe. Worth flagging: scripted scores often overvalue polished infrastructure and undervalue organic chaos. The catch? That chaos is exactly what separates a deep experience from a surface-level one.

“I automated ninety percent of the scoring pipeline. The ten percent I saved on humans cost me two trips that felt like theme parks.”

— Operations lead for a boutique travel platform, after a messy off-season audit

How often should I update a depth score? (At least seasonally, or before any major event)

Every six months is the floor — every quarter if the destination sees fast-moving change. Monsoon reroutes flights. A festival turns a sleepy town into a circus for three weeks. New construction can gut the very backstreet charm that earned a high score the year before. Most teams skip this until something breaks, then scramble to re-score when a client complains.

Set a calendar trigger. Before peak season. Before a known mega-event. And always after a natural disaster or political shift. The score itself is not a monument; it is a snapshot. Run it stale and you are better off guessing. That said, do not update so often that the data noise drowns out signal — monthly tweaks for a place that changes slowly only add confusion. Pick a rhythm. Stick to it until the destination proves it needs more attention.

What if my score contradicts a well-known guidebook? (Debate it — that's the point)

Good. A depth score that parrots Lonely Planet or Rick Steves adds nothing. The whole exercise is meant to surface what those broad-stroke guides miss — the narrow alleyway that floods during spring melt, the family-run taverna that only serves one dish, the neighborhood where safety shifts block by block. A contradiction means you found something they did not.

But do not be arrogant about it. Cross-check your assumptions: did you weight authenticity too heavily while ignoring accessibility? Did the guidebook capture a baseline that you overlooked? The most honest move is to publish the tension — note where your score disagrees and why. Travelers appreciate transparency over a polished, unassailable number. A single paragraph explaining the gap builds more trust than a perfect score that smells sanitized.

Apply Your Depth Score to Plan Your Next Trip

Pick three candidate destinations and run the full workflow

You have spent an hour scoring, maybe two. Now choose three places you actually might visit next season—not fantasy islands, not bucket-list boasts. Real contenders. For each, run the complete workflow from section three: gather your six data fields, compute the depth value, then overlay your personal constraint weightings. Do not cheat by skipping the recalibration step. The trick most people miss? They score one destination, love the number, and stop. Three forces comparison. You will see one score that looks robust until you realize it hides a 14-hour transit day. That hurts—better to catch it now than at check-in.

According to practitioners we interviewed, the trade-off is rarely about talent — it is about handoffs, and however confident you feel after the first pass, the pitfall shows up when someone else repeats your shortcut without the same context.

Produce a single-page deliverable: one A4 sheet per candidate, handwritten if possible. Each sheet shows the raw score, the adjusted score, and three bullet notes on what the depth actually costs you in time or flexibility. I have watched travelers fixate on a 92-point score only to discover the destination required three separate accommodations. Depth without structure is just another number.

The short version is simple: fix the order before you optimize speed.

Compare your depth scores against standard peak-season itineraries

Pull up three popular blog itineraries for those same destinations—typical 7-day plans from travel sites that never mention depth scoring. Lay your score next to their schedule. The gap is the real insight. A standard Barcelona itinerary might promise "relaxed tapas crawls," but your depth score flags that every major site sits 40 minutes apart by metro during August crowds. The conventional plan assumes flow; your score measures friction.

According to practitioners we interviewed, the trade-off is rarely about talent — it is about handoffs, and however confident you feel after the first pass, the pitfall shows up when someone else repeats your shortcut without the same context.

That sounds fine until you realize the conventional plan costs you two extra hours daily in transfer time—hours you never recover. Mark the mismatch directly on your deliverable sheet. Standard plan says 3 museums in one day.

Pause here first.

Depth score says 2 max with buffer. That concrete annotation becomes your trip's boundary condition. Most guidebooks skip this—they sell aspiration, not logistics.

'I scored Kyoto at 78 but every standard itinerary packed five temples daily. My actual trip? Three per day. I stopped feeling rushed around day two.'

— reader submission after beta-testing depth scoring on a spring trip

Book one trip based on the depth score and document the difference

Commit. Pick the destination where the depth score and your constraint weightings align most cleanly—not the highest raw number, the most honest fit. Book it. Then journal the entire thing. Not a vacation diary—a logistics log. Each evening, write three lines: what the depth score predicted, what actually happened, and one surprise. The value emerges only after the trip ends, when you compare planned depth versus experienced depth.

What usually breaks first is the transit buffer. Your score assumed 20-minute metro waits; reality delivered 35. That gap teaches you how to adjust your scoring inputs for next time. I keep a folder of these trip logs from five different destinations. Each one refined the weight I assign to connectivity speed versus cultural density. Worth flagging—the first trip log always feels thin.

It adds up fast.

Three lines feels like nothing. After three trips, you have a calibration dataset that no guidebook offers. That is the point. Not a perfect score, but a scoring system that learns from your actual feet-on-ground experience. Start today. Pick those three candidates before you close this tab.